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[
  {
    "content": "Here's a 4-week content schedule focusing on the key insights and trends in AI for 2024:\n\n### Week 1: \n**Theme: AI Integration and Development Tools**\n\n**Post 1:**\n- **Title:** \"Unleashing Developer Productivity: AI-Driven Code Completion with Claude Code IDE\"\n- **Content:** Explore how AI tools are being integrated into development environments like Emacs. Learn about the Claude Code IDE's role in enhancing developer productivity with AI-assisted code completion.\n- **Link:** [Read more](https://github.com/manzaltu/claude-code-ide.el)\n\n**Post 2:**\n- **Title:** \"Asynchronous Coding Agents: A New Frontier with Google’s Jules\"\n- **Content:** Dive into the concept of asynchronous coding agents and how Google’s Jules is revolutionizing AI's role in coding processes to drive productivity.\n- **Link:** [Explore the blog post](https://blog.google/technology/google-labs/jules-now-available/)\n\n**Post 3:**\n- **Title:** \"AI Integration in Search Engines: Enhancing User Experience

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[
  {
    "content": "Based on the provided AI trends for 2024, here is a content schedule plan for 4 weeks, focusing on 3 posts per week. Each week targets a specific aspect of AI development and its implications.\n\n### Week 1: Open AI Models and Tools\n\n- **Post 1: The Rise of Kitten TTS** \n  - Discuss the potential of lightweight, open-source text-to-speech models like Kitten TTS and their implications for device accessibility.\n\n- **Post 2: Transparency in AI with OpenAI's Models**\n  - Explore OpenAI's initiative for open AI models and how this transparency can impact AI research.\n\n- **Post 3: Optimizing AI with Ollama Turbo**\n  - Highlight the significance of resource management tools like Ollama Turbo in AI system training and deployment.\n\n### Week 2: Advanced AI Technologies\n\n- **Post 1: Understanding Genie 3 from DeepMind**\n  - An overview of DeepMind's Genie 3 and its impact on interaction models for robotics and automation.\n\n- **Post 2: Innovations in Drone Technology**\n  - Discuss t

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[
  {
    "content": "Je vais bien, merci ! Comment puis-je vous aider aujourd'hui ?",
    "content_type": "str",
    "metrics": {
      "input_tokens": 81,
      "output_tokens": 14,
      "total_tokens": 95,
      "prompt_tokens": 81,
      "completion_tokens": 14,

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[
  {
    "content": "### Content Schedule for AI Trends in 2024\n\n**Week 1: Focus on Conversational AI and Model Evolutions**\n\n- **Post 1: Monday**\n  - **Topic**: The Future of Conversational AI in 2024\n  - **Content**: Overview of conversational AI growth, including market projections and potential sectors benefiting from advancements.\n  - **Research Source**: Forbes\n  - **Engagement**: Poll asking which industry will benefit the most from conversational AI.\n\n- **Post 2: Wednesday**\n  - **Topic**: Challenges of AI Model Evolution\n  - **Content**: Discuss the convergence in AI model development and the challenges in evaluating model intelligence. \n  - **Research Source**: MSN\n  - **Engagement**: Share an infographic explaining the evolution of AI models.\n\n- **Post 3: Friday**\n  - **Topic**: Introduction to AI Retrieval Models\n  - **Content**: Explain Gemini embeddings and their role in enhancing retrieval-augmented generation models.\n  - **Research Source**: Hackernews\n  - **Engagement**: 

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from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.storage.sqlite import SqliteStorage
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.hackernews import HackerNewsTools
from agno.workflow.v2.step import Step, StepInput, StepOutput
from agno.workflow.v2.workflow import Workflow

# Define agents

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"""Please install dependencies using:
pip install openai newspaper4k lxml_html_clean agno
"""

import json
from typing import Iterator

import httpx
from agno.agent import Agent, RunResponse
from agno.tools.newspaper4k import Newspaper4kTools

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from agno.agent import Agent
from agno.knowledge.text import TextKnowledgeBase
from agno.utils.media import (
    SampleDataFileExtension,
    download_knowledge_filters_sample_data,
)
from agno.vectordb.lancedb import LanceDb


def initialize_vector_db(table_name="recipes", uri="tmp/lancedb"):

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"""Run `pip install duckduckgo-search` to install dependencies."""

from agno.agent import Agent
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.models.litellm import LiteLLM

# Make sure the model used supports functional tool calling like openai, better llama models, etc
agent = Agent(
    model=LiteLLM(id="openrouter/meta-llama/llama-4-maverick"),
    tools=[DuckDuckGoTools()],

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# %% [markdown]
# # Documentation for Current Notebook
# This notebook is designed to demonstrate the creation and usage of an SEO Agent for optimizing web content. The agent leverages various tools and APIs, such as SERP API, Google Search Tools, ExaTools, to enhance content visibility on search engines.
#
# ## Key Components:
# 1. **SERP API Tool**: Fetches search engine results and extracts trending keywords.
# 2. **SEO Agent**: A custom agent built using the `agno` library to process and optimize content based on SEO best practices.
# 3. **Environment Variables**: Uses `.env` file to securely store API keys.
# 4. **Input and Output**: Accepts JSON input containing HTML tags and content, processes it, and returns optimized content in JSON format.
#