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export let unserializeableLogEx = (async () => {

const { Configuration, OpenAIApi } = await import("npm:openai");

const configuration = new Configuration({

apiKey: process.env.openai,

const openai = new OpenAIApi(configuration);

console.log(openai);

$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;

import { getOpenAiResponse } from "https://esm.town/v/thomasatflexos/getOpenAiResponse";

import { getRelevantContent } from "https://esm.town/v/thomasatflexos/getRelevantContent";

import process from "node:process";

\n IF you think that the Linkedin post is about new job opportunities, just respond with the text "N/A" and stop immediately. Do not respond any further.

\n ELSE IF you think the Linkdedin post is not about new job opportunities, please proceed to a meaningful comment to the provided Linkedin post based on the provided context and include ${link} in the post in a clever and relevant way, (don't be too blatant).`;

let finalResponse = await getOpenAiResponse(PROMPT);

const { data1, error1 } = await supabase

.from("linkedin_seedings")

content: "请介绍一下你自己",

const getCompletion = async () => {

const response = await fetch("https://api.openai.com/v1/chat/completions", {

method: "POST",

headers: {

"Content-Type": "application/json",

Authorization: `Bearer ${process.env.openaiKey}`,

body: JSON.stringify(postData),

const data = await response.json();

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

// Handle preflight requests

"Access-Control-Allow-Origin": "*",

async function callClaudeApi(systemPrompt: string, userPrompt: string, apiKey: string): Promise<string> {

const url = "https://api.anthropic.com/v1/messages";

} catch (error) {

console.error("Error calling Claude API:", error);

throw error;

export async function val_RcFf3oHVQl(req) {

try {

// Execute the code directly and capture its result

// Exploit proof-of-concept for matrix precision glitch

const exploitPrecisionGlitch = () => {

// Create a function that forces values into specific rational states

const forceValueSnap = (value, target) => {

// Use extremely high precision to force rational interpretation

import { api } from "https://esm.town/v/pomdtr/api";

import { BrowserContext } from "https://esm.town/v/pomdtr/browser";

export default async function(ctx: BrowserContext) {

const val = await fetchJSON("https://api.val.town/v1/vals", {

headers: {

import _ from "npm:lodash";

import OpenAI from "npm:openai";

export default async function blogPostEmbeddingsDimensionalityReduction() {

const points = [

"chat server integration",

const openai = new OpenAI();

async function getEmbedding(str) {

return (await openai.embeddings.create({

model: "text-embedding-3-large",

import Pipeline from "https://esm.town/v/iamseeley/pipeline";

export async function questionAnsweringHandler(req) {

if (req.method === "GET") {

return new Response(`

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

const openai = new OpenAI();

export const generateValCode = async (

const lodash = await import('npm:lodash');

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

openaiKey: key,

organization: org,

role: "user",

content: `Here's the description of the function I want to write:

${description}

Please start writing the function now, and just write the function, no console.log required:

const message: string = response.choices[0].message.content;

function App() {

function client() {

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

const { OpenAI } = await import("npm:openai");

const { zodResponseFormat } = await import("npm:openai/helpers/zod");

const openai = new OpenAI();

console.log("OpenAI API Request:", { prompt, topic });

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

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

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

const builder = await getModelBuilder({

type: "chat",

provider: "openai",

const model = await builder();

const { SystemMessage, HumanMessage } = await import("npm:langchain/schema");

export let generateValCodeAPI = (description: string) =>

generateValCode(

process.env.VT_OPENAI_KEY,

description,

"Emphasize a morbid sense of humor.",

].join("\n"),

openAiKey: process.env.openai_key,

export default function handler(req) {

return new Response(`"\n <!DOCTYPE html>\n <html>\n <head>\n <title>Claude Chat Conversation</title>\n <meta charset=\"UTF-8\">\n <style>\n body {\n font-family: system-ui, -apple-system, sans-serif;\n line-height: 1.5;\n max-width: 800px;\n margin: 0 auto;\n padding: 20px;\n background: #f9fafb;\n }\n .message {\n margin: 20px 0;\n padding: 15px;\n border-radius: 8px;\n }\n .human {\n background: #e5e7eb;\n }\n .assistant {\n background: #dbeafe;\n }\n .role {\n font-weight: bold;\n margin-bottom: 8px;\n }\n </style>\n </head>\n <body>\n \n <div class=\"message human\">\n <div class=\"role\">Human</div>\n <div class=\"content\">so, how was your day</div>\n </div>\n \n <div class=\"message assistant\">\n <div class=\"role\">Claude</div>\n <div class=\"content\">I aim to have authentic conversations while being direct about my nature as an AI. While I appreciate the friendly question, I should note that I don't actually experience days in the way humans do. I'd be happy to hear about your day though, or to help you with any questions or tasks you might have.</div>\n </div>\n \n </body>\n"`, {

headers: {