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import { OpenAI } from "https://esm.town/v/std/openai";

const openai = new OpenAI();

export default async function(req: Request): Promise<Response> {

log.info("Prepared messages for OpenAI:", JSON.stringify(messages));

const openai = new OpenAI();

const stream = await openai.chat.completions.create({

log.info("OpenAI stream created successfully");

log.error("Error in OpenAI request:", error);

export async function parseRequest(req: Request): Promise<{ selection: string; followUp: string; context: string[] }> {

export function prepareMessages(selection: string, followUp: string, context: string[]): Array<Object> {

export async function val_f4POmlAgdz(req) {

try {

// Execute the code directly and capture its result

import { OpenAI } from "https://esm.sh/openai";

import { zodResponseFormat } from "https://esm.sh/openai/helpers/zod";

import { z } from "https://esm.sh/zod";

}, { concurrency: 3 });

const openai = new OpenAI();

const ValDescriptions = z.object({

description: z.string(),

async function getDescriptions(val) {

const completion = await openai.beta.chat.completions.parse({

model: "gpt-4o-mini",

}, { concurrency: 3 });

async function getScreenshot(url: string) {

const resp = await fetch(

browserWSEndpoint: `wss://chrome.browserless.io?token=${process.env.browserlessKey}`,

const page = await browser.newPage();

await page.goto("https://en.wikipedia.org/wiki/OpenAI");

const intro = await page.evaluate(

`document.querySelector('p:nth-of-type(2)').innerText`,

export function denoServer(handler: (req: Request) => Response | Promise<Response>, thisArg?: any) {

if (thisArg) {

handler = handler.bind(thisArg);

export function myApi(name) {

return "hi " + name;

import { Hono } from "npm:hono@3";

import OpenAI from "npm:openai";

import { renderToString } from "npm:react-dom/server";

return new Response(renderToString(jsx), { headers: { "Content-Type": "text/html" } });

function renderForm(

valName: string,

const textEncoder = new TextEncoder();

function write(text) {

writer.write(textEncoder.encode(text));

const scrollingElement = document.body;

const callback = function (mutationsList, observer) {

window.scrollTo({ left: 0, top: document.body.scrollHeight, behavior: "instant" });

content:

role: "user",

</div>,

const openai = new OpenAI();

const stream = await openai.chat.completions.create({

model,

import { InvocationTracker } from 'https://esm.town/v/rozek/InvocationTracker'

import { floatingQuotaTracker } from 'https://esm.town/v/rozek/floatingQuotaTracker'

export default async function (Request:Request):Promise<Response> {

if (Request.method === 'OPTIONS') {

return new Response(null, {

$error: "Run completed without signaling end or error.",

export const service = (boardUrl: string, options?: ServiceOptions) => {

return async function(req: Request): Promise<Response> {

const url = new URL(req.url);

const path = url.pathname;

export default async function (req: Request): Promise<Response> {

<p>This schema defines several components that can be used to integrate OpenAI's services into a logging and configuration system. Here's how each component relates to potential OpenAI use cases:</p>

<li><strong>App</strong>: Represents an OpenAI-powered application with a name and URL.</li>

<li><strong>Config</strong>: Defines configuration options for OpenAI API integration and application settings.</li>

<li><strong>ConsoleLog</strong>: Captures console output from OpenAI model interactions and application processes.</li>

<li><strong>CronLog</strong>: Logs scheduled tasks related to OpenAI operations, such as model fine-tuning or dataset updates.</li>

<li><strong>HttpLog</strong>: Records HTTP requests made to and from the OpenAI API.</li>

<button class="collapsible-button">Key Components and OpenAI Use Cases</button>

Use Case: Store OpenAI API keys, model preferences, and application settings.

Example: Track rate limits, response times, and payload sizes for OpenAI API calls.

export async function val_BfYl3Zckp6(req) {

try {

// Execute the code directly and capture its result

const result = await (async () => {

function probeComputationalLimits(depth = 0, maxDepth = 1000) {

if (depth >= maxDepth) {

return {

const TrackingTable = 'OpenRouter_Test'

const Granularity = 15*60*1000

export default async function (Request:Request):Promise<Response> {

if (Request.method !== 'GET') {

return new Response('Method Not Allowed', { status:405 })

import axios from "npm:axios";

const TARGET = 1000 * 10;

async function loop() {

var url = "https://cyrilos-loop.web.val.run";

await axios({

url: url,

async function playDice() {

const PLATFORM = "polpick.io";

const cookies = await blob.getJSON("platforms");

transition: background-color 0.5s ease;

function AnimatedHeadline() {

const [animationStarted, setAnimationStarted] = useState(false);

transition: width 2.5s linear;

function SubheadlineAnimation() {

const [animationStarted, setAnimationStarted] = useState(false);

opacity: 0.8;

function App() {

const [isNavOpen, setIsNavOpen] = React.useState(false);

</Footer>

function client() {

createRoot(document.getElementById("root")).render(<App />);

if (typeof document !== "undefined") { client(); }

export default async function server(request: Request): Promise<Response> {

return new Response(`

import { OpenAI } from "https://esm.town/v/std/openai";

const openai = new OpenAI();

async function handleMessage(message) {

async function processUserInputWithAI(userMessage: string, tasks: Task[], state: UserState): Promise<{ response: string, updatedTasks: Task[], updatedState: UserState, suggestedActions: string[] }> {

You can use the following functions:

const completion = await openai.chat.completions.create({

function addTask(tasks: Task[], title: string) {

function markTaskAsDone(tasks: Task[], taskId: string) {

function breakDownTask(tasks: Task[], taskId: string, subtasks: string[]) {

function findTask(tasks: Task[], taskId: string): Task | null {