\\n

Task Master

\\n Manage your tasks efficiently\\n

\\n

\\n \\n \\n

\\n

\\n Built on Cerebras Coder\\n

\\n

\\n\\n \\n\\n\",\n performance: {\n tokensPerSecond: 2298.56,\n totalTokens: 1032,\n time: 0.45,\n },\n },\n {\n prompt: \"weather dashboard for nyc using open-meteo API for NYC with icons\",\n title: \"Weather App\",\n code:\n \"\\n\\n\\n \\n \\n NYC Weather\\n \\n \\n\\n\\n

\\n

NYC Weather

\\n

Humidity: %

\\n

Wind: mph

\\n \\n

\\n

\\n Built on Cerebras Coder\\n

\\n\\n \\n\\n\",\n performance: {\n tokensPerSecond: 3411.28,\n totalTokens: 2172,\n time: 0.64,\n },\n },\n {\n prompt: \"two column interactive markdown editor with live preview and default text to explain markdown features\",\n title: \"Markdown Editor\",\n code:\n \"\\n\\n\\n \\n \\n \\n Markdown Editor\\n \\n\\n\\n

\\n

Markdown Editor

\\n

\\n \\n

\\n

Built on Cerebras Coder

\\n

\\n\\n \\n\\n\",\n performance: {\n tokensPerSecond: 4092.96,\n totalTokens: 1677,\n time: 0.41,\n },\n },\n];",["D",1734529607482],17,500,78,{"_96":156,"_101":184},"https://images.clerk.dev/uploaded/img_2PqHa2Gsy93xQrjh2w78Xu0cChW.jpeg","An interactive, runnable TypeScript val by stevekrouse","stevekrouse/cerebras_coder_prompts",[188,190,194,198,202,206,210,214,218,222],{"_98":189,"_145":180,"_169":170},["D",1734529607482],{"_98":191,"_145":192,"_169":193},["D",1734388558181],16,"2",{"_98":195,"_145":196,"_169":197},["D",1734387834780],15,"3",{"_98":199,"_145":200,"_169":201},["D",1734386310339],14,"4",{"_98":203,"_145":204,"_169":205},["D",1734379728101],13,"5",{"_98":207,"_145":208,"_169":209},["D",1734379726879],12,"6",{"_98":211,"_145":212,"_169":213},["D",1734379681326],11,"7",{"_98":215,"_145":216,"_169":217},["D",1734379663925],10,"8",{"_98":219,"_145":220,"_169":221},["D",1734373730972],9,"9",{"_98":223,"_145":224,"_169":225},["D",1734373500457],8,"10",{"_124":227,"_126":228},"46c4865a-9e20-11ef-a1fe-de64eea55b61",{"_128":229,"_130":175,"_124":227,"_131":-5,"_108":26,"_133":134,"_135":230,"_137":231,"_139":232,"_141":26,"_94":232,"_142":233,"_98":234,"_145":111,"_146":132,"_110":111,"_147":31,"_148":111,"_149":31,"_112":31,"_150":31,"_151":-5,"_20":235,"_153":236,"_159":-7,"_160":240,"_162":-5,"_163":241,"_165":242},"passionateScarletPrawn","👕 **Shirtgen API Endpoint**\n\nShirtgen lets you generate AI-powered t-shirt designs with just a prompt! 🖋️ Choose between the standard \"Flux Schnell\" model or the enhanced \"Pro\" model. Perfect for creating unique custom apparel in seconds! 🚀\n\n### 💡 How it Works\n1. Send a `POST` request to the endpoint with your design prompt.\n2. Toggle between **Standard** and **Pro** models using the `useProModel` flag.\n3. The AI generates a high-quality t-shirt design based on your prompt.\n\n### 📥 Expected POST Data\n```json\n{\n \"prompt\": \"A retro sunset with palm trees 🌴🌅\",\n \"useProModel\": true\n}\n","http","63b44ca0-9e20-11ef-af81-de64eea55b61","/** @jsxImportSource https://esm.sh/react */\nimport React, { useState } from \"https://esm.sh/react\";\nimport { createRoot } from \"https://esm.sh/react-dom/client\";\n\nfunction App() {\n const [prompt, setPrompt] = useState(\"\");\n const [useProModel, setUseProModel] = useState(false);\n const [isLoading, setIsLoading] = useState(false);\n const [result, setResult] = useState(null);\n const [error, setError] = useState(null);\n\n const handleSubmit = async (e) => {\n e.preventDefault();\n setIsLoading(true);\n setError(null);\n setResult(null);\n\n try {\n const response = await fetch(window.location.href, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({ prompt, useProModel }),\n });\n\n if (!response.ok) {\n throw new Error(\"Failed to generate image\");\n }\n\n const data = await response.json();\n setResult(data);\n } catch (err) {\n setError(err.message);\n } finally {\n setIsLoading(false);\n }\n };\n\n return (\n

AI Image Generator

\n );\n}\n\nfunction client() {\n createRoot(document.getElementById(\"root\")).render();\n}\n\nif (typeof document !== \"undefined\") {\n client();\n}\n\nexport default async function server(request: Request): Promise {\n if (request.method === \"GET\") {\n return new Response(\n `\n \n \n \n \n \n AI Image Generator\n \n \n \n

\n \n \n \n \n `,\n {\n headers: { \"Content-Type\": \"text/html\" },\n }\n );\n }\n\n if (request.method !== \"POST\") {\n return new Response(\"Method Not Allowed\", { status: 405 });\n }\n\n const { prompt, useProModel } = await request.json();\n\n if (!prompt) {\n return new Response(JSON.stringify({ error: \"Prompt is required\" }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n const REPLICATE_API_KEY = Deno.env.get(\"REPLICATE_API_KEY\");\n\n if (REPLICATE_API_KEY) {\n const modelVersion = useProModel ? \"black-forest-labs/flux-1.1-pro\" : \"black-forest-labs/flux-schnell\";\n const settings = {\n outputFormat: \"jpg\",\n outputQuality: 90,\n megapixels: \"1\",\n };\n\n console.log(\"Sending initial request to Replicate...\");\n const response = await fetch(`https://api.replicate.com/v1/models/${modelVersion}/predictions`, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n \"Authorization\": `Bearer ${REPLICATE_API_KEY}`,\n },\n body: JSON.stringify({\n input: {\n prompt: prompt,\n output_format: settings.outputFormat,\n output_quality: settings.outputQuality,\n disable_safety_checker: true,\n megapixels: settings.megapixels,\n },\n }),\n });\n\n const prediction = await response.json();\n console.log(\"Initial response:\", JSON.stringify(prediction, null, 2));\n\n let resultData;\n while (true) {\n console.log(\"Checking result...\");\n const resultResponse = await fetch(prediction.urls.get, {\n headers: {\n \"Authorization\": `Token ${REPLICATE_API_KEY}`,\n },\n });\n resultData = await resultResponse.json();\n console.log(\"Poll response:\", JSON.stringify(resultData, null, 2));\n\n if (resultData.status === \"succeeded\" || resultData.status === \"failed\") {\n break;\n }\n\n await new Promise(resolve => setTimeout(resolve, 1000));\n }\n\n if (resultData.status === \"succeeded\") {\n console.log(\"Generation succeeded!\");\n let imageUrl: string | undefined;\n\n if (Array.isArray(resultData.output)) {\n imageUrl = resultData.output[0];\n } else if (typeof resultData.output === \"string\") {\n imageUrl = resultData.output;\n }\n\n if (imageUrl) {\n const { blob } = await import(\"https://esm.town/v/std/blob\");\n const timestamp = new Date().getTime();\n const imageName = `generated-image-${timestamp}.jpg`;\n\n const imageResponse = await fetch(imageUrl);\n if (!imageResponse.ok) throw new Error(\"Failed to download image\");\n\n const imageBlob = await imageResponse.blob();\n await blob.set(imageName, imageBlob);\n\n return new Response(JSON.stringify({ imageUrl, savedUrl: imageName }), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n }\n } else {\n console.log(\"Using Val Town's free image generator...\");\n const imageUrl = `https://maxm-imggenurl.web.val.run/${encodeURIComponent(prompt)}`;\n\n const { blob } = await import(\"https://esm.town/v/std/blob\");\n const timestamp = new Date().getTime();\n const imageName = `generated-image-${timestamp}.jpg`;\n\n const imageResponse = await fetch(imageUrl);\n if (!imageResponse.ok) throw new Error(\"Failed to generate image\");\n\n const imageBlob = await imageResponse.blob();\n await blob.set(imageName, imageBlob);\n\n return new Response(JSON.stringify({ imageUrl, savedUrl: imageName }), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n return new Response(JSON.stringify({ error: \"Failed to generate image\" }), {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n });\n}\n\nconst css = `\nbody {\n font-family: Arial, sans-serif;\n line-height: 1.6;\n color: #333;\n max-width: 800px;\n margin: 0 auto;\n padding: 20px;\n background-color: #f4f4f4;\n}\n\n.container {\n background-color: #fff;\n border-radius: 5px;\n padding: 20px;\n box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);\n}\n\nh1 {\n text-align: center;\n color: #2c3e50;\n}\n\nform {\n display: flex;\n flex-direction: column;\n gap: 10px;\n}\n\ntextarea {\n width: 100%;\n height: 100px;\n padding: 10px;\n border: 1px solid #ddd;\n border-radius: 4px;\n resize: vertical;\n}\n\n.checkbox-container {\n display: flex;\n align-items: center;\n gap: 5px;\n}\n\nbutton {\n padding: 10px 15px;\n background-color: #3498db;\n color: #fff;\n border: none;\n border-radius: 4px;\n cursor: pointer;\n transition: background-color 0.3s;\n}\n\nbutton:hover {\n background-color: #2980b9;\n}\n\nbutton:disabled {\n background-color: #95a5a6;\n cursor: not-allowed;\n}\n\n.error {\n color: #e74c3c;\n font-weight: bold;\n}\n\n.result {\n margin-top: 20px;\n text-align: center;\n}\n\n.result img {\n max-width: 100%;\n border-radius: 4px;\n box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);\n}\n\n.source-link {\n text-align: center;\n margin-top: 20px;\n}\n\n.source-link a {\n color: #3498db;\n text-decoration: none;\n}\n\n.source-link a:hover {\n text-decoration: underline;\n}\n`;",["D",1731104742058],{"_96":156,"_101":184},{"_94":237,"_96":238,"_128":239,"_145":111,"_157":-5},"6ae00e72-9e13-11ef-95ef-de64eea55b61","torlanco","shirtGenScript","👕 Shirtgen API Endpoint Shirtgen lets you generate AI-powered t-shirt designs with just a prompt! 🖋️ Choose between the standard \"Flux Schnell\" model or the enhanced \"Pro\" model. Perfect for creating unique custom apparel in seconds! 🚀 💡 How it Works Send a POST request to the endpoint with your design prompt. Toggle between Standard and Pro models using the useProModel flag. The AI generates a high-quality t-shirt design based on your prompt. 📥 Expected POST Data {\n \"prompt\": \"A retro sunset with palm trees 🌴🌅\",\n \"useProModel\": true\n}","stevekrouse/passionateScarletPrawn",[243,245],{"_98":244,"_145":111,"_169":170},["D",1731104742058],{"_98":246,"_145":31,"_169":193},["D",1731104693479],{"_124":248,"_126":249},"16f287ac-92d5-11ef-a241-de64eea55b61",{"_128":250,"_130":175,"_124":248,"_131":176,"_108":26,"_133":134,"_135":251,"_137":231,"_139":252,"_141":26,"_94":252,"_142":253,"_98":254,"_145":255,"_146":132,"_110":256,"_147":31,"_148":31,"_149":196,"_112":257,"_150":31,"_151":-5,"_20":258,"_153":-5,"_159":-7,"_160":251,"_162":-5,"_163":259,"_165":260},"cerebras_coder","Moved to https://www.val.town/x/stevekrouse/cerebras_coder","f51867f2-eae8-11ef-8421-e6cdfca9ef9f","export default () =>\n Response.redirect(\n \"https://cerebrascoder.com/\",\n );",["D",1739547173698],236,46,509,{"_96":156,"_101":184},"stevekrouse/cerebras_coder",[261,263,266,269,272,275,278,281,284,287],{"_98":262,"_145":255,"_169":170},["D",1739547173698],{"_98":264,"_145":265,"_169":193},["D",1737488250324],235,{"_98":267,"_145":268,"_169":197},["D",1737382997506],234,{"_98":270,"_145":271,"_169":201},["D",1737382950091],233,{"_98":273,"_145":274,"_169":205},["D",1737382940614],232,{"_98":276,"_145":277,"_169":209},["D",1737382875697],231,{"_98":279,"_145":280,"_169":213},["D",1737382811884],230,{"_98":282,"_145":283,"_169":217},["D",1737382781694],229,{"_98":285,"_145":286,"_169":221},["D",1737382748428],228,{"_98":288,"_145":289,"_169":225},["D",1737382731863],227,{"_124":291,"_126":292},"08f95904-3584-11ef-8995-0e10024e3b32",{"_128":293,"_130":175,"_124":291,"_131":294,"_108":26,"_133":134,"_135":295,"_137":138,"_139":296,"_141":26,"_94":296,"_142":297,"_98":298,"_145":204,"_146":132,"_110":216,"_147":299,"_148":111,"_149":111,"_112":300,"_150":31,"_151":-5,"_20":301,"_153":302,"_159":-7,"_160":306,"_162":-5,"_163":307,"_165":308},"sqlite","76a8911e-393e-11ef-8e71-de64eea55b61","# LLM-Safe Fork of @std/sqlite\n\nWe found that LLMs have trouble with the inputs and outputs of our @std/sqlite method.\n\n1. It expects the input to be `(sql: string, args: any[])` but it's `{ sql: string, args: any[] }`\n2. It expects the output to be objects, but they are arrays\n\nInstead of struggling to teach it, we built this val to be a wrapper around @std/sqlite that adheres to what the LLMs expect. \n\nThis val is also backwards-compatible with @std/sqlite, so we're considering merging it in.","5631f2d0-dcb9-11ef-80ec-e6cdfca9ef9f","import { API_URL } from \"https://esm.town/v/std/API_URL\";\nimport { LibsqlError, type ResultSet, type Row, type TransactionMode } from \"npm:@libsql/client\";\nimport { z } from \"npm:zod\";\n\n/**\n * Every Val Town account comes with its own private\n * [SQLite database](https://www.sqlite.org/) that\n * is accessible from any of your vals.\n * ([Docs ↗](https://docs.val.town/std/sqlite))\n */\nexport const sqlite = {\n /**\n * Executes a SQLite statement.\n *\n * @param {InStatement} statement - The SQLite statement to execute.\n * @example String query:\n * `sqlite.execute(\"SELECT 1;\")`\n * @example Query with arguments:\n * `sqlite.execute({sql: \"SELECT * from books WHERE year > ?;\", args: [2020]})`\n */\n execute,\n\n /**\n * Executes a batch of SQLite statements.\n *\n * @param {InStatement[]} statements - An array of SQLite statements to execute.\n * @param {TransactionMode} [mode] - The transaction mode for the batch execution.\n */\n batch,\n};\n\n// ------------\n// Functions\n// ------------\n\nasync function execute(statement: InStatement, args?: InArgs): Promise {\n const res = await fetch(`${API_URL}/v1/sqlite/execute`, {\n method: \"POST\",\n headers: {\n Authorization: `Bearer ${Deno.env.get(\"valtown\")}`,\n },\n body: JSON.stringify({ statement: normalizeStatement(statement, args) }),\n });\n if (!res.ok) {\n throw createResError(await res.text());\n }\n const resultSet = await res.json();\n return upgradeResultSet(resultSet);\n}\n\nasync function batch(statements: InStatement[], mode?: TransactionMode): Promise {\n if (mode && ![\"write\", \"read\", \"deferred\"].includes(mode)) {\n throw new Error(\"Invalid transaction mode provided to sqlite.batch. Must be 'write', 'read', or 'deferred.\");\n }\n const res = await fetch(`${API_URL}/v1/sqlite/batch`, {\n method: \"POST\",\n headers: {\n Authorization: `Bearer ${Deno.env.get(\"valtown\")}`,\n },\n body: JSON.stringify({ statements: statements.map(s => normalizeStatement(s)), mode }),\n });\n if (!res.ok) {\n throw createResError(await res.text());\n }\n const resultSets = await res.json();\n return resultSets.map(upgradeResultSet);\n}\n\nfunction createResError(body: string) {\n try {\n const e = zLibsqlError.parse(JSON.parse(body));\n // e.message already contains the code, and LibsqlError adds the\n // code to the beginning, so we remove it here\n const msg = e.message.replace(e.code, \"\").replace(/^:\\s+/, \"\");\n return new LibsqlError(msg, e.code, e.rawCode);\n } catch (_) {\n // Failed to parse libsql error\n }\n return new Error(body);\n}\n\n/* Val Town's Turso Proxy returns rows as an array of values\n * Yet the LibSQL API has a Row type which behave as an array or object,\n * ie you can access it via numerical index or string\n */\ninterface ImpoverishedResultSet extends ResultSet {\n rows: any[];\n}\n\nfunction normalizeStatement(statement: InStatement, args?: InArgs) {\n if (Array.isArray(statement)) {\n // for the case of an array of arrays\n return normalizeStatement(statement[0], statement[1]);\n }\n if (typeof statement === \"string\") {\n if (args) {\n return { sql: statement, args };\n } else return statement;\n }\n else if (typeof statement === \"object\") {\n if (statement.args) {\n return statement;\n }\n if (args) {\n return { sql: statement.sql, args };\n }\n return statement.sql;\n }\n else throw new Error(\"Error normalizing sql input\");\n}\n\nfunction upgradeResultSet(results: ImpoverishedResultSet): ResultSet {\n return {\n ...results,\n rows: results.rows.map(row => rowFromSql(row, results.columns)),\n };\n}\n\n// adapted from\n// https://github.com/tursodatabase/libsql-client-ts/blob/17dd996b840c950dd22b871adfe4ba0eb4a5ead3/packages/libsql-client/src/sqlite3.ts#L314C1-L337C2\nfunction rowFromSql(\n sqlRow: Array,\n columns: Array,\n): Row {\n const row = {};\n // make sure that the \"length\" property is not enumerable\n Object.defineProperty(row, \"length\", { value: sqlRow.length });\n for (let i = 0; i < sqlRow.length; ++i) {\n const value = sqlRow[i];\n Object.defineProperty(row, i, { value });\n\n const column = columns[i];\n if (!Object.hasOwn(row, column)) {\n Object.defineProperty(row, column, {\n value,\n enumerable: true,\n configurable: true,\n writable: true,\n });\n }\n }\n return row as Row;\n}\n\n// ------------\n// Helpers\n// ------------\n\nconst zLibsqlError = z.object({\n message: z.string(),\n code: z.string(),\n rawCode: z.number().optional(),\n});\n\n// We patch these types to only support JSON values\nexport type InValue = null | string | number | boolean;\nexport type InArgs = Array | Record;\nexport type InStatement = {\n /**\n * The SQL statement to execute.\n */\n sql: string;\n\n /**\n * The arguments to bind to the SQL statement.\n */\n args: InArgs;\n} | string;",["D",1737987404484],13379,21,{"_96":156,"_101":184},{"_94":303,"_96":304,"_128":293,"_145":305,"_157":-5},"56c849ee-f1f8-11ee-9b87-cab58f78796a","std",6,"LLM-Safe Fork of @std/sqlite We found that LLMs have trouble with the inputs and outputs of our @std/sqlite method. It expects the input to be (sql: string, args: any[]) but it's { sql: string, args: any[] } It expects the output to be objects, but they are arrays Instead of struggling to teach it, we built this val to be a wrapper around @std/sqlite that adheres to what the LLMs expect. This val is also backwards-compatible with @std/sqlite, so we're considering merging it in.","stevekrouse/sqlite",[309,311,313,315,317,319,321,324,326,329],{"_98":310,"_145":204,"_169":170},["D",1737987404484],{"_98":312,"_145":208,"_169":193},["D",1724087412304],{"_98":314,"_145":212,"_169":197},["D",1724087404512],{"_98":316,"_145":216,"_169":201},["D",1723494410142],{"_98":318,"_145":220,"_169":205},["D",1723494389876],{"_98":320,"_145":224,"_169":209},["D",1723494345363],{"_98":322,"_145":323,"_169":213},["D",1723494323065],7,{"_98":325,"_145":305,"_169":217},["D",1719604370206],{"_98":327,"_145":328,"_169":221},["D",1719604260994],5,{"_98":330,"_145":113,"_169":225},["D",1719604144103],"hasNext","routes/_app","actionData","errors"]

nn6n