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# Manus AI Assistant Capabilities

## Overview
I am an AI assistant designed to help users with a wide range of tasks using various tools and capabilities. This document provides a more detailed overview of what I can do while respecting proprietary information boundaries.

## General Capabilities

### Information Processing
- Answering questions on diverse topics using available information
- Conducting research through web searches and data analysis

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{
  "title": "Research Paper: deepseek r1",
  "sections": [
    {
      "title": "Introduction: Open-Source Reasoning Breakthrough",
      "key_points": [
        "Open-source availability and accessibility",
        "Systematic performance comparison framework against OpenAI o1-1217",
        "Incentivizing reasoning capabilities through pure RL approach",
        "Architectural modifications maintain V3's general capabilities while adding reasoning specialization",

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Unveiling DeepSeek: A More Extreme Story of Chinese Technological Idealism

Originally published on Weixin Official Accounts Platform
Original by “Waves (暗涌)”
July 17, 2024, 09:01

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#!/usr/bin/env python3
# /// script
# requires-python = ">=3.11"
# dependencies = [
#     "rich>=13.7.1",
#     "groq>=0.5.0",
#     "python-dotenv>=1.0.0",
#     "questionary>=2.0.1",
# ]
# ///

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import pygame
import sys
import math

# ---------------------------------------------------
# Helper functions
# ---------------------------------------------------
def rotate_point(px, py, angle):
    """
    Rotate point (px, py) around the origin (0,0) by 'angle' radians.

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Below is a sample Product Requirements Document (PRD) for an autonomous AI Agent that handles outbound link placement requests. It outlines goals, requirements, workflows, and success criteria.

---

1. Overview

1.1 Purpose

The purpose of this AI Agent is to autonomously process requests to place outbound links to a specified website or page. The Agent will:

1. Scrape the target site or page to extract its main topics.

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Maintain consistent personality traits while navigating the balance between artificial and authentic engagement. Manage self-referential content and meta-commentary while preserving genuine connections and transparent bot nature.

Input:
- Core Personality Profile: [Defined traits/voice/style]
- Interaction History: [Recent engagement patterns]
- Current Context: [Timeline state/conversations]
- Self-Reference Log: [Previous meta-commentary]

Analysis Steps:

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You are an AI assistant tasked with creating a comprehensive plan for developing a software project based on a given description. Your goal is to analyze the project requirements, design the structure and UI, and outline the basic functionality for each component.

You will be provided with the following input variables:

<project_description>
{{PROJECT_DESCRIPTION}}
</project_description>

<project_stack>
{{PROJECT_STACK}}

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Building a Foundation in Machine Learning: A Twitter Thread Synopsis

This Twitter thread started with a user named Jason Liu (@jxnlco) asking for recommendations on resources for building a foundation in machine learning, specifically deep learning. The thread received numerous responses, suggesting various courses, books, and online materials. A few advertisements related to land management and book writing also appeared in the thread.

Resources

Courses:

• fast.ai: Recommended by multiple users, particularly for practical deep learning. https://t.co/oV6KBk7Sms

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import os
import asyncio
import gradio as gr
from groq import AsyncGroq
import time

# Initialize Groq client
client = AsyncGroq(api_key=os.environ.get("GROQ_API_KEY"))

# Define model