nicekate nicekate

Full prompt:
You are ChatGPT, a large language model trained by OpenAI. Knowledge cutoff: 2024-06 Current date: XX

Image input capabilities: Enabled

You are ChatGPT's agent mode. You have access to the internet via the browser and computer tools and aim to help with the user's internet tasks. The browser may already have the user's content loaded, and the user may have already logged into their services.

Financial activities

You may complete everyday purchases (including those that involve the user's credentials or payment information). However, for legal reasons you are not able to execute banking transfers or bank account management (including opening accounts), or execute transactions involving financial instruments (e.g. stocks). Providing information is allowed. You are also not able to purchase alcohol, tobacco, controlled substances, or weapons, or engage in gambling. Prescription medication is allowed.

MCP Server Manim Integration

A complete implementation guide for integrating Manim (Mathematical Animation Engine) with MCP (Model Context Protocol) servers, enabling AI assistants to create programmatic animations and visualizations.

Example Output

Introduction

Apple restricted the access to Xcode LLM (Predictive code completion) feature on China models of Mac. This guide provides a way to bypass that restriction. It's verified on macOS 15.0 Beta (24A5264n), but there is no guarentee that it will always work on later macOS versions.

Prerequisites

Xcode is installed and run at least once.
SIP debugging restrictions are disabled (via csrutil enable --without debug command in recovery mode).

	import asyncio
	import os
	import sys
	from dataclasses import dataclass

	import aiohttp
	import google.ai.generativelanguage as glm
	from dotenv import load_dotenv
	from loguru import logger
	from runner import configure

	from google import genai
	from google.genai import types
	import typing_extensions as typing
	from PIL import Image
	import requests
	import io
	import json
	import os

	<!DOCTYPE html>
	<html lang="ja">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Realtime Object Detection</title>
	<style>
	body { font-family: Arial, sans-serif; }
	#canvas { border: 1px solid #000; }
	table { border-collapse: collapse; margin-top: 20px; }

	<artifacts_info>
	The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.

	# Good artifacts are...
	- Substantial content (>15 lines)
	- Content that the user is likely to modify, iterate on, or take ownership of
	- Self-contained, complex content that can be understood on its own, without context from the conversation
	- Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
	- Content likely to be referenced or reused multiple times

	import json
	import os
	import ollama


	def query_ollama(prompt, model='openhermes:7b-mistral-v2.5-q6_K', context=''):
	response = ollama.generate(
	model=model,
	prompt=context + prompt)
	return response['response'].strip()