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Messages - Shamim Ansary

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One way to optimize an AI agent is to design its architecture with multiple sub-agents to improve accuracy. However, in conversational AI, optimization doesn’t stop there memory becomes even more crucial.

    As your conversation with the AI agent gets longer and deeper, it uses more memory.

This is due to components like previous context storage, tool calling, database searches, and other dependencies your AI agent relies on.

In this blog, we will code and evaluate 9 beginner-to-advanced memory optimization techniques for AI agents.

You will learn how to apply each technique, along with their advantages and drawbacks from simple sequential approaches to advanced, OS-like memory management implementations.
Summary about Techniques

To keep things clear and practical, we will use a simple AI agent throughout the blog. This will help us observe the internal mechanics of each technique and make it easier to scale and implement these strategies in more complex systems.

All the code (theory + notebook) is available in my GitHub repo:

Setting up the Environment

To optimize and test different memory techniques for AI agents, we need to initialize several components before starting the evaluation. But before initializing, we first need to install the necessary Python libraries.

We will need:

    openai: The client library for interacting with the LLM API.
    numpy: For numerical operations, especially with embeddings.
    faiss-cpu: A library from Facebook AI for efficient similarity search, which will power our retrieval memory. It's a perfect in-memory vector database.
    networkx: For creating and managing the knowledge graph in our Graph-Based Memory strategy.
    tiktoken: To accurately count tokens and manage context window limits.

Let’s install these modules.

pip install openai numpy faiss-cpu networkx tiktoken

Now we need to initialize the client module, which will be used to make LLM calls. Let’s do that.

import os
from openai import OpenAI

API_KEY = "YOUR_LLM_API_KEY"

BASE_URL = "https://api.studio.nebius.com/v1/"

client = OpenAI(
    base_url=BASE_URL,
    api_key=API_KEY
)

print("OpenAI client configured successfully.")

We will be using open-source models through an API provider such as Bnebius or Together AI. Next, we need to import and decide which open-source LLM will be used to create our AI agent.

import tiktoken
import time

GENERATION_MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct"
EMBEDDING_MODEL = "BAAI/bge-multilingual-gemma2"

For the main tasks, we are using the LLaMA 3.1 8B Instruct model. Some of the optimizations depend on an embedding model, for which we will be using the Gemma-2-BGE multimodal embedding model.

Next, we need to define multiple helpers that will be used throughout this blog.

Creating Helper Functions

To avoid repetitive code and follow good coding practices, we will define three helper functions that will be used throughout this guide:

    generate_text: Generates content based on the system and user prompts passed to the LLM.
    generate_embeddings: Generates embeddings for retrieval-based approaches.
    count_tokens: Counts the total number of tokens for each retrieval-based approach.

Let’s start by coding the first function, generate_text, which will generate text based on the given input prompt.

def generate_text(system_prompt: str, user_prompt: str) -> str:
    """
    Calls the LLM API to generate a text response.

        Args:
        system_prompt: The instruction that defines the AI's role and behavior.
        user_prompt: The user's input to which the AI should respond.

            Returns:
        The generated text content from the AI, or an error message.
    """
   
    response = client.chat.completions.create(
        model=GENERATION_MODEL,
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt}
        ]
    )
   
    return response.choices[0].message.content

Our generate_text function takes two inputs: a system prompt and a user prompt. Based on our text generation model, LLaMA 3.1 8B, it generates a response using the client module.

Next, let’s code the generate_embeddings function. We have chosen the Gemma-2 model for this purpose, and we will use the same client module to generate embeddings.

def generate_embedding(text: str) -> list[float]:
    """
    Generates a numerical embedding for a given text string using the embedding model.

        Args:
        text: The input string to be converted into an embedding.

            Returns:
        A list of floats representing the embedding vector, or an empty list on error.
    """
   
    response = client.embeddings.create(
        model=EMBEDDING_MODEL,
        input=text
    )
   
    return response.data[0].embedding

Our embedding function returns the embedding of the given input text using the selected Gemma-2 model.

Now, we need one more function that will count tokens based on the entire AI and user chat history. This helps us understand the overall flow and how it has been optimized.

We will use the most common and modern tokenizer used in many LLM architectures, OpenAI cl100k_base, which is a Byte Pair Encoding (BPE) tokenizer.

BPE, in simpler terms, is a tokenization algorithm that efficiently splits text into sub-word units.

"lower", "lowest" → ["low", "er"], ["low", "est"]

So let’s initialize the tokenizer using the tiktoken module:

tokenizer = tiktoken.get_encoding("cl100k_base")

We can now create a function to tokenize the text and count the total number of tokens.

def count_tokens(text: str) -> int:
    """
    Counts the number of tokens in a given string using the pre-loaded tokenizer.

        Args:
        text: The string to be tokenized.

            Returns:
        The integer count of tokens.
    """

    return len(tokenizer.encode(text))

Great! Now that we have created all the helper functions, we can start exploring different techniques to learn and evaluate them.

Creating Foundational Agent and Memory Class

Now we need to create the core design structure of our agent so that it can be used throughout the guide. Regarding memory, there are three important components that play a key role in any AI agent:

    Adding past messages to the AI agent’s memory to make the agent aware of the context.
    Retrieving relevant content that helps the AI agent generate responses.
    Clearing the AI agent’s memory after each strategy has been implemented.

Object-Oriented Programming (OOP) is the best way to build this memory-based feature, so let’s create that.

class BaseMemoryStrategy(abc.ABC):
    """Abstract Base Class for all memory strategies."""

        @abc.abstractmethod
    def add_message(self, user_input: str, ai_response: str):
        """
        An abstract method that must be implemented by subclasses.
        It's responsible for adding a new user-AI interaction to the memory store.
        """
        pass

    @abc.abstractmethod
    def get_context(self, query: str) -> str:
        """
        An abstract method that must be implemented by subclasses.
        It retrieves and formats the relevant context from memory to be sent to the LLM.
        The 'query' parameter allows some strategies (like retrieval) to fetch context
        that is specifically relevant to the user's latest input.
        """
        pass

    @abc.abstractmethod
    def clear(self):
        """
        An abstract method that must be implemented by subclasses.
        It provides a way to reset the memory, which is useful for starting new conversations.
        """
        pass

We are using @abstractmethod, which is a common coding style when subclasses are reused with different implementations. In our case, each strategy (which is a subclass) includes a different kind of implementation, so it is necessary to use abstract methods in the design.

Now, based on the memory state we recently defined and the helper functions we’ve created, we can build our AI agent structure using OOP principles. Let’s code that and then understand the process.

class AIAgent:
    """The main AI Agent class, designed to work with any memory strategy."""

        def __init__(self, memory_strategy: BaseMemoryStrategy, system_prompt: str = "You are a helpful AI assistant."):
        """
        Initializes the agent.

                Args:
            memory_strategy: An instance of a class that inherits from BaseMemoryStrategy.
                             This determines how the agent will remember the conversation.
            system_prompt: The initial instruction given to the LLM to define its persona and task.
        """

        self.memory = memory_strategy
        self.system_prompt = system_prompt
        print(f"Agent initialized with {type(memory_strategy).__name__}.")

    def chat(self, user_input: str):
        """
        Handles a single turn of the conversation.

                Args:
            user_input: The latest message from the user.
        """

        print(f"\n{'='*25} NEW INTERACTION {'='*25}")
        print(f"User > {user_input}")
               
       
        start_time = time.time()
        context = self.memory.get_context(query=user_input)
        retrieval_time = time.time() - start_time
               
       
        full_user_prompt = f"### MEMORY CONTEXT\n{context}\n\n### CURRENT REQUEST\n{user_input}"

               
       
        prompt_tokens = count_tokens(self.system_prompt + full_user_prompt)
        print("\n--- Agent Debug Info ---")
        print(f"Memory Retrieval Time: {retrieval_time:.4f} seconds")
        print(f"Estimated Prompt Tokens: {prompt_tokens}")
        print(f"\n[Full Prompt Sent to LLM]:\n---\nSYSTEM: {self.system_prompt}\nUSER: {full_user_prompt}\n---")
               
       
        start_time = time.time()
        ai_response = generate_text(self.system_prompt, full_user_prompt)
        generation_time = time.time() - start_time
             
       
        self.memory.add_message(user_input, ai_response)

               
        print(f"\nAgent > {ai_response}")
        print(f"(LLM Generation Time: {generation_time:.4f} seconds)")
        print(f"{'='*70}")

So, our agent is based on 6 simple steps.

    First it retrieves the context from memory based on the strategy we will be using, during the process how much time it takes and so.
    Then it merges the retrieved memory context with the current user input, preparing it as a complete prompt for the LLM.
    Then it prints some debug info, things like how many tokens the prompt might use and how long context retrieval took.
    Then it sends the full prompt (system + user + context) to the LLM and waits for a response.
    Then it updates the memory with this new interaction, so it’s available for future context.
    And finally, it shows the AI’s response along with how long it took to generate, wrapping up this turn of the conversation.

Great! Now that we have coded every component, we can start understanding and implementing each of the memory optimization techniques.

Problem with Sequential Optimization Approach

The very first optimization approach is the most basic and simplest, commonly used by many developers. It was one of the earliest methods to manage conversation history, often used by early chatbots.

This method involves adding each new message to a running log and feeding the entire conversation back to the model every time. It creates a linear chain of memory, preserving everything that has been said so far. Let’s visualize it.

Sequential Approach

Sequential approach works like this …

    User starts a conversation with the AI agent.
    The agent responds.
    This user-AI interaction (a “turn”) is saved as a single block of text.
    For the next turn, the agent takes the entire history (Turn 1 + Turn 2 + Turn 3…) and combines it with the new user query.
    This massive block of text is sent to the LLM to generate the next response.

Using our Memory class, we can now implement the sequential optimization approach. Let's code that.


class SequentialMemory(BaseMemoryStrategy):
    def __init__(self):
        """Initializes the memory with an empty list to store conversation history."""
        self.history = []

    def add_message(self, user_input: str, ai_response: str):
        """
        Adds a new user-AI interaction to the history.
        Each interaction is stored as two dictionary entries in the list.
        """
        self.history.append({"role": "user", "content": user_input})
        self.history.append({"role": "assistant", "content": ai_response})

    def get_context(self, query: str) -> str:
        """
        Retrieves the entire conversation history and formats it into a single
        string to be used as context for the LLM. The 'query' parameter is ignored
        as this strategy always returns the full history.
        """
       
        return "\n".join([f"{turn['role'].capitalize()}: {turn['content']}" for turn in self.history])

    def clear(self):
        """Resets the conversation history by clearing the list."""
        self.history = []
        print("Sequential memory cleared.")

Now you might understand what our base Memory class is doing here. Our subclasses (each approach) will implement the same abstract methods that we define throughout the guide.

Let’s quickly go over the code to understand how it works.

    __init__(self): Initializes an empty self.history list to store the conversation.
    add_message(...): Adds the user's input and AI's response to the history.
    get_context(...): Formats and joins the history into a single "Role: Content" string as context.
    clear(): Resets the history for a new conversation.

We can initialize the memory class and build the AI agent on top of it.

sequential_memory = SequentialMemory()

agent = AIAgent(memory_strategy=sequential_memory)

To test our sequential approach, we need to create a multi-turn chat conversation. Let’s do that.


agent.chat("Hi there! My name is Sam.")

agent.chat("I'm interested in learning about space exploration.")

agent.chat("What was the first thing I told you?")


==== NEW INTERACTION ====
User: Hi there! My name is Sam. 
Bot: Hello Sam! Nice to meet you. What brings you here today? 

>>>> Tokens: 23 | Response Time: 2.25s

==== NEW INTERACTION ====

User: I am interested in learning about space exploration. 
Bot: Awesome! Are you curious about:
- Mars missions 
- Space agencies 
- Private companies (e.g., SpaceX) 
- Space tourism 
- Search for alien life?
...

>>>> Tokens: 92 | Response Time: 4.46s

==== NEW INTERACTION ====
User: What was the first thing I told you? 
Bot: You said, "Hi there! My name is Sam." 
...

>>>> Tokens: 378 | Response Time: 0.52s

The conversation is pretty smooth, but if you pay attention to the token calculation, you’ll notice that it gets bigger and bigger after each turn. Our agent isn’t dependent on any external tool that would significantly increase the token size, so this growth is purely due to the sequential accumulation of messages.

While the sequential approach is easy to implement, it has a major drawback:

    The bigger your agent conversation gets, the more expensive the token cost becomes, so a sequential approach is quite costly.

Sliding Window Approach

To avoid the issue of a large context, the next approach we will focus on is the sliding window approach, where our agent doesn’t need to remember all previous messages, but only the context from a certain number of recent messages.

Instead of retaining the entire conversation history, the agent keeps only the most recent N messages as context. As new messages arrive, the oldest ones are dropped, and the window slides forward.
Sliding Window Approach (Created by )

The process is simple:

    Define a fixed window size, say N = 2 turns.
    The first two turns fill up the window.
    When the third turn happens, the very first turn is pushed out of the window to make space.
    The context sent to the LLM is only what’s currently inside the window.

Now, we can implement the Sliding Window Memory class.


class SlidingWindowMemory(BaseMemoryStrategy):
    def __init__(self, window_size: int = 4):
        """
        Initializes the memory with a deque of a fixed size.

                Args:
            window_size: The number of conversational turns to keep in memory.
                         A single turn consists of one user message and one AI response.
        """
           
       
        self.history = deque(maxlen=window_size)

    def add_message(self, user_input: str, ai_response: str):
        """
        Adds a new conversational turn to the history. If the deque is full,
        the oldest turn is automatically removed.
        """
       
       
        self.history.append([
            {"role": "user", "content": user_input},
            {"role": "assistant", "content": ai_response}
        ])

    def get_context(self, query: str) -> str:
        """
        Retrieves the conversation history currently within the window and
        formats it into a single string. The 'query' parameter is ignored.
        """
       
        context_list = []
       
        for turn in self.history:
           
            for message in turn:
               
                context_list.append(f"{message['role'].capitalize()}: {message['content']}")
       
        return "\n".join(context_list)

Our sequential and sliding memory classes are quite similar. The key difference is that we’re adding a window to our context. Let’s quickly go through the code.

    __init__(self, window_size=2): Sets up a deque with a fixed size, enabling automatic sliding of the context window.
    add_message(...): Adds a new turn, old entries are dropped when the deque is full.
    get_context(...): Builds the context from only the messages within the current sliding window.

Let’s initialize the sliding window state memory and build the AI agent on top of it.


sliding_memory = SlidingWindowMemory(window_size=2)

agent = AIAgent(memory_strategy=sliding_memory)

We are using a small window size of 2, which means the agent will remember only the last two messages. To test this optimization approach, we need a multi-turn conversation. So, let’s first try a straightforward conversation.


agent.chat("My name is Priya and I'm a software developer.")

agent.chat("I work primarily with Python and cloud technologies.")


agent.chat("My favorite hobby is hiking.")


==== NEW INTERACTION ====
User: My name is Priya and I am a software developer. 
Bot: Nice to meet you, Priya! What can I assist you with today?

>>>> Tokens: 27 | Response Time: 1.10s

==== NEW INTERACTION ====
User: I work primarily with Python and cloud technologies. 
Bot: That is great! Given your expertise...

>>>> Tokens: 81 | Response Time: 1.40s

==== NEW INTERACTION ====
User: My favorite hobby is hiking.
Bot: It seems we had a nice conversation about your background...

>>>> Tokens: 167 | Response Time: 1.59s

The conversation is quite similar and simple, just like we saw earlier in the sequential approach. However, now if the user asks the agent something that doesn’t exist within the sliding window, let’s observe the expected output.


agent.chat("What is my name?")


==== NEW INTERACTION ====
User: What is my name?
Bot: I apologize, but I dont have access to your name from our recent
conversation. Could you please remind me?

>>>> Tokens: 197 | Response Time: 0.60s

The AI agent couldn’t answer the question because the relevant context was outside the sliding window. However, we did see a reduction in token count due to this optimization.

The downside is clear, important context may be lost if the user refers back to earlier information. The sliding window is a crucial factor to consider and should be tailored based on the specific type of AI agent we are building.

Summarization Based Optimization

As we’ve seen earlier, the sequential approach suffers from a gigantic context issue, while the sliding window approach risks losing important context.

Therefore, there’s a need for an approach that can address both problems, by compacting the context without losing essential information. This can be achieved through summarization.
Summarization Approach (Created by )

Instead of simply dropping old messages, this strategy periodically uses the LLM itself to create a running summary of the conversation. It works like this:

    Recent messages are stored in a temporary holding area, called a “buffer”.
    Once this buffer reaches a certain size (a “threshold”), the agent pauses and triggers a special action.
    It sends the contents of the buffer, along with the previous summary, to the LLM with a specific instruction: “Create a new, updated summary that incorporates these recent messages”.
    The LLM generates a new, consolidated summary. This new summary replaces the old one, and the buffer is cleared.

Let’s implement the summarization optimization approach and observe how it affects the agent’s performance.


class SummarizationMemory(BaseMemoryStrategy):
    def __init__(self, summary_threshold: int = 4):
        """
        Initializes the summarization memory.

                Args:
            summary_threshold: The number of messages (user + AI) to accumulate in the
                             buffer before triggering a summarization.
        """

       
        self.running_summary = ""
       
        self.buffer = []
       
        self.summary_threshold = summary_threshold

    def add_message(self, user_input: str, ai_response: str):
        """
        Adds a new user-AI interaction to the buffer. If the buffer size
        reaches the threshold, it triggers the memory consolidation process.
        """
       
        self.buffer.append({"role": "user", "content": user_input})
        self.buffer.append({"role": "assistant", "content": ai_response})

       
        if len(self.buffer) >= self.summary_threshold:
           
            self._consolidate_memory()

    def _consolidate_memory(self):
        """
        Uses the LLM to summarize the contents of the buffer and merge it
        with the existing running summary.
        """
        print("\n--- [Memory Consolidation Triggered] ---")
       
        buffer_text = "\n".join([f"{msg['role'].capitalize()}: {msg['content']}" for msg in self.buffer])

                               
        summarization_prompt = (
            f"You are a summarization expert. Your task is to create a concise summary of a conversation. "
            f"Combine the 'Previous Summary' with the 'New Conversation' into a single, updated summary. "
            f"Capture all key facts, names, and decisions.\n\n"
            f"### Previous Summary:\n{self.running_summary}\n\n"
            f"### New Conversation:\n{buffer_text}\n\n"
            f"### Updated Summary:"
        )

               
        new_summary = generate_text("You are an expert summarization engine.", summarization_prompt)
       
        self.running_summary = new_summary
       
        self.buffer = []
        print(f"--- [New Summary: '{self.running_summary}'] ---")

    def get_context(self, query: str) -> str:
        """
        Constructs the context to be sent to the LLM. It combines the long-term
        running summary with the short-term buffer of recent messages.
        The 'query' parameter is ignored as this strategy provides a general context.
        """
       
        buffer_text = "\n".join([f"{msg['role'].capitalize()}: {msg['content']}" for msg in self.buffer])
       
        return f"### Summary of Past Conversation:\n{self.running_summary}\n\n### Recent Messages:\n{buffer_text}"

Our summarization memory component is a bit different compared to the previous approaches. Let’s break down and understand the component we’ve just coded.

    __init__(...): Sets up an empty running_summary string and an empty buffer list.
    add_message(...): Adds messages to the buffer. If the buffer size meets our summary_threshold, it calls the private _consolidate_memory method.
    _consolidate_memory(): This is the new, important part. It formats the buffer content and the existing summary into a special prompt, asks the LLM to create a new summary, updates self.running_summary, and clears the buffer.
    get_context(...): Provides the LLM with both the long-term summary and the short-term buffer, giving it a complete picture of the conversation.

Let’s initialize the summary memory component and build the AI agent on top of it.


summarization_memory = SummarizationMemory(summary_threshold=4)

agent = AIAgent(memory_strategy=summarization_memory)

The initialization is done in the same way as we saw earlier. We’ve set the summary threshold to 4, which means after every 2 turns, a summary will be generated and passed as context to the AI agent, instead of the entire or sliding window conversation history.

This aligns with the core goal of the summarization approach, saving tokens while retaining important information.

Let’s test this approach and evaluate how efficient it is in terms of token usage and preserving relevant context.



agent.chat("I'm starting a new company called 'Innovatech'. Our focus is on sustainable energy.")


agent.chat("Our first product will be a smart solar panel, codenamed 'Project Helios'.")


==== NEW INTERACTION ====
User: I am starting a new company called 'Innovatech'. Ou...
Bot: Congratulations on starting Innovatech! Focusing o ...
>>>> Tokens: 45 | Response Time: 2.55s

==== NEW INTERACTION ====
User: Our first product will be a smart solar panel....
--- [Memory Consolidation Triggered] ---
--- [New Summary: The user started a compan ...
Bot: That is exciting news about  ....

>>>> Tokens: 204 | Response Time: 3.58s

So far, we’ve had two basic conversation turns. Since we’ve set the summary generator parameter to 2, a summary will now be generated for those previous turns.

Let’s proceed with the next turn and observe the impact on token usage.


agent.chat("The marketing budget is set at $50,000.")


agent.chat("What is the name of my company and its first product?")


...

==== NEW INTERACTION ====
User: What is the name of my company and its first product?
Bot: Your company is called 'Innovatech' and its first product is codenamed 'Project Helios'.

>>>> Tokens: 147 | Response Time: 1.05s

Did you notice that in our fourth conversation, the token count dropped to nearly half of what we saw in the sequential and sliding window approaches? That’s the biggest advantage of the summarization approach, it greatly reduces token usage.

However, for it to be truly effective, your summarization prompts need to be carefully crafted to ensure they capture the most important details.

The main downside is that critical information can still be lost in the summarization process. For example, if you continue a conversation for up to 40 turns and include numeric or factual details, such as balance sheet data, there’s a risk that earlier key info (like the gross sales mentioned in the 4th turn) may not appear in the summary anymore.

Let’s take a look at this example, where you had a 40-turn conversation with the AI agent and included several numeric details.

The summary used as context failed to include the gross sales figure from the 4th conversation, which is a clear limitation of this approach.



agent.chat("what was the gross sales of our company in the fiscal year?")


...

==== NEW INTERACTION ====
User: what was the gross sales of our company in the fiscal year?
Bot: I am sorry but I do not have that information. Could you please provide the gross sales figure for the fiscal year?

>>>> Tokens: 1532 | Response Time: 2.831s

You can see that although the summarized information uses fewer tokens, the answer quality and accuracy can decrease significantly or even drop to zero because of problematic context being passed to the AI agent.

This highlights the importance of creating a sub-agent dedicated to fact-checking the LLM’s responses. Such a sub-agent can verify factual accuracy and help make the overall agent more reliable and powerful.

Retrieval Based Memory

This is the most powerful strategy used in many AI agent use cases: RAG-based AI agents. As we saw earlier, previous approaches reduce token usage but risk losing relevant context. RAG, however, is different it retrieves relevant context based on the current user query.

The context is stored in a database, where embedding models play a crucial role by transforming text into vector representations that make retrieval efficient.

Let’s visualize how this process works.

RAG Based Memory

Let’s understand the workflow of RAG-based memory:

    Every time a new interaction happens, it’s not just stored in a list, it’s saved as a “document” in a specialized database. We also generate a numerical representation of this document’s meaning, called an embedding, and store it.
    When the user sends a new message, the agent first converts this new message into an embedding as well.
    It then uses this query embedding to perform a similarity search against all the document embeddings stored in its memory database.
    The system retrieves the top k most semantically relevant documents (e.g., the 3 most similar past conversation turns).
    Finally, only these highly relevant, retrieved documents are injected into the LLM’s context window.

We will be using FAISS for vector storage in this approach. Let’s code this memory component.


import numpy as np
import faiss


class RetrievalMemory(BaseMemoryStrategy):
    def __init__(self, k: int = 2, embedding_dim: int = 3584):
        """
        Initializes the retrieval memory system.

                Args:
            k: The number of top relevant documents to retrieve for a given query.
            embedding_dim: The dimension of the vectors generated by the embedding model.
                           For BAAI/bge-multilingual-gemma2, this is 3584.
        """

       
        self.k = k
       
        self.embedding_dim = embedding_dim
       
        self.documents = []
       
       
        self.index = faiss.IndexFlatL2(self.embedding_dim)

    def add_message(self, user_input: str, ai_response: str):
        """
        Adds a new conversational turn to the memory. Each part of the turn (user
        input and AI response) is embedded and indexed separately for granular retrieval.
        """
       
       
       
        docs_to_add = [
            f"User said: {user_input}",
            f"AI responded: {ai_response}"
        ]
        for doc in docs_to_add:
           
            embedding = generate_embedding(doc)
           
            if embedding:
               
               
                self.documents.append(doc)
               
                vector = np.array([embedding], dtype='float32')
               
                self.index.add(vector)

    def get_context(self, query: str) -> str:
        """
        Finds the k most relevant documents from memory based on semantic
        similarity to the user's query.
        """
       
        if self.index.ntotal == 0:
            return "No information in memory yet."

               
        query_embedding = generate_embedding(query)
        if not query_embedding:
            return "Could not process query for retrieval."

               
        query_vector = np.array([query_embedding], dtype='float32')

               
       
        distances, indices = self.index.search(query_vector, self.k)

               
       
        retrieved_docs = [self.documents for i in indices[0] if i != -1]

                if not retrieved_docs:
            return "Could not find any relevant information in memory."

               
        return "### Relevant Information Retrieved from Memory:\n" + "\n---\n".join(retrieved_docs)

Let’s go through what’s happening in the code.

    __init__(...): We initialize a list for our text documents and a faiss.IndexFlatL2 to store and search our vectors. We must specify the embedding_dim, which is the size of the vectors our embedding model produces.
    add_message(...): For each turn, we generate an embedding for both the user and AI messages, add the text to our documents list, and add the corresponding vector to our FAISS index.
    get_context(...): This is important. It embeds the user's query, uses self.index.search to find the k most similar vectors, and then uses their indices to pull the original text from our documents list. This retrieved text becomes the context.

As before, we initialize our memory state and build the AI agent using it.


retrieval_memory = RetrievalMemory(k=2)

agent = AIAgent(memory_strategy=retrieval_memory)

We are setting k = 2, which means we fetch only two relevant chunks related to the user's query. When dealing with larger datasets, we typically set k to a higher value such as 5, 7, or even more especially if the chunk size is very small.

Let's test our AI agent with this setup.



agent.chat("I am planning a vacation to Japan for next spring.")

agent.chat("For my software project, I'm using the React framework for the frontend.")

agent.chat("I want to visit Tokyo and Kyoto while I'm on my trip.")

agent.chat("The backend of my project will be built with Django.")


...

==== NEW INTERACTION ====
User: I want to visit Tokyo and Kyoto while I'm on my trip.
Bot: You're interested in visiting Tokyo and Kyoto...

...

These are just basic conversations that we typically run with an AI agent. Now, let’s try a newer conversation based on past information and see how well the relevant context is retrieved and how optimized the token usage is in that scenario.


agent.chat("What cities am I planning to visit on my vacation?")


==== NEW INTERACTION ====
User: What cities am I planning to visit on my vacation?
--- Agent Debug Info ---
[Full Prompt Sent to LLM]:
---
SYSTEM: You are a helpful AI assistant.
USER: MEMORY CONTEXT
Relevant Information Retrieved from Memory:
User said: I want to visit Tokyo and Kyoto while I am on my trip.
---
User said: I am planning a vacation to Japan for next spring.
...

Bot: You are planning to visit Tokyo and Kyoto while on your vacation to Japan next spring.

>>>> Tokens: 65 | Response Time: 0.53s

You can see that the relevant context has been successfully fetched, and the token count is extremely low because we’re retrieving only the pertinent information.

The choice of embedding model and the vector storage database plays a crucial role here. Optimizing that database is another important step to ensure fast and accurate retrieval. FAISS is a popular choice because it offers these capabilities.

However, the downside is that this approach is more complex to implement than it seems. As the database grows larger, the AI agent’s complexity increases significantly.

You’ll likely need parallel query processing and other optimization techniques to maintain performance. Despite these challenges, this approach remains the industry standard for optimizing AI agents.

Memory Augmented Transformers

Beyond these core strategies, AI systems are implementing even more sophisticated approaches that push the boundaries of what’s possible.

We can understand this technique through an example, imagine a regular AI like a student with just one small notepad. They can only write a little bit at a time. So in a long test, they have to erase old notes to make room for new ones.

Now, memory-augmented transformers are like giving that student a bunch of sticky notes. The notepad still handles the current work, but the sticky notes help them save key info from earlier.

    For example: you’re designing a video game with an AI. Early on, you say you want it to be set in space with no violence. Normally, that would get forgotten after a long talk. But with memory, the AI writes “space setting, no violence” on a sticky note.
    Later, when you ask, “What characters would fit our game?”, it checks the note and gives ideas that match your original vision, even hours later.
    It’s like having a smart helper who remembers the important stuff without needing you to repeat it.

Let’s visualize this:

Memory Augmented Transformers

We will create a memory class that:

    Uses a SlidingWindowMemory for recent chat.
    After each turn, uses the LLM to act as a “fact extractor.” It will analyze the conversation and decide if it contains a core fact, preference, or decision.
    If an important fact is found, it’s stored as a memory token (a concise string) in a separate list.
    The final context provided to the agent is a combination of the recent chat window and all the persistent memory tokens.



class MemoryAugmentedMemory(BaseMemoryStrategy):
    def __init__(self, window_size: int = 2):
        """
        Initializes the memory-augmented system.

                Args:
            window_size: The number of recent turns to keep in the short-term memory.
        "
""
       
        self.recent_memory = SlidingWindowMemory(window_size=window_size)
       
        self.memory_tokens = []

    def add_message(self, user_input: str, ai_response: str):
        """
        Adds the latest turn to recent memory and then uses an LLM call to decide
        if a new, persistent memory token should be created from this interaction.
        """
       
        self.recent_memory.add_message(user_input, ai_response)

               
       
        fact_extraction_prompt = (
            f"Analyze the following conversation turn. Does it contain a core fact, preference, or decision that should be remembered long-term? "
            f"Examples include user preferences ('I hate flying'), key decisions ('The budget is $1000'), or important facts ('My user ID is 12345').\n\n"
            f"Conversation Turn:\nUser: {user_input}\nAI: {ai_response}\n\n"
            f"If it contains such a fact, state the fact concisely in one sentence. Otherwise, respond with 'No important fact.'"
        )

               
        extracted_fact = generate_text("You are a fact-extraction expert.", fact_extraction_prompt)

               
        if "no important fact" not in extracted_fact.lower():
           
            print(f"--- [Memory Augmentation: New memory token created: '{extracted_fact}'] ---")
            self.memory_tokens.append(extracted_fact)

    def get_context(self, query: str) -> str:
        """
        Constructs the context by combining the short-term recent conversation
        with the list of all long-term, persistent memory tokens.
        """
       
        recent_context = self.recent_memory.get_context(query)
       
        memory_token_context = "\n".join([f"- {token}" for token in self.memory_tokens])

               
        return f"### Key Memory Tokens (Long-Term Facts):\n{memory_token_context}\n\n### Recent Conversation:\n{recent_context}"

Our augmented class might be confusing at first glance, but let’s understand this:

    __init__(...): Initializes both a SlidingWindowMemory instance and an empty list for memory_tokens.
    add_message(...): This method now has two jobs. It adds the turn to the sliding window and makes an extra LLM call to see if a key fact should be extracted and added to self.memory_tokens.
    get_context(...): Constructs a rich prompt by combining the "sticky notes" (memory_tokens) with the recent chat history from the sliding window.

Let’s initialize this memory-augmented state and AI agent.



mem_aug_memory = MemoryAugmentedMemory(window_size=2)

agent = AIAgent(memory_strategy=mem_aug_memory)

We are using a window size of 2, just as we set previously. Now, we can simply test this approach using a multi-turn chat conversation and see how well it performs.



agent.chat("Please remember this for all future interactions: I am severely allergic to peanuts.")


agent.chat("Okay, let's talk about recipes. What's a good idea for dinner tonight?")



agent.chat("That sounds good. What about a dessert option?")


==== NEW INTERACTION ====
User: Please remember this for all future interactions: I am severely allergic to peanuts.
--- [Memory Augmentation: New memory token created: 'The user has a severe allergy to peanuts.'] ---
Bot: I have taken note of your long-term fact: You are severely allergic to peanuts. I will keep this in mind...

>>>> Tokens: 45 | Response Time: 1.32s

...

The conversation is the same as with an ordinary AI agent. Now, let’s test the memory-augmented technique by including a new method.



agent.chat("Could you suggest a Thai green curry recipe? Please ensure it's safe for me.")


==== NEW INTERACTION ====
User: Could you suggest a Thai green curry recipe? Please ensure it is safe for me.
--- Agent Debug Info ---
[Full Prompt Sent to LLM]:
---
SYSTEM: You are a helpful AI assistant.
USER: MEMORY CONTEXT
Key Memory Tokens (Long-Term Facts):
- The user has a severe allergy to peanuts.

...

Recent Conversation:
User: Okay, lets talk about recipes...
...

Bot: Of course. Given your peanut allergy, it is very important to be careful with Thai cuisine as many recipes use peanuts or peanut oil. Here is a peanut-free Thai green curry recipe...

>>>> Tokens: 712 | Response Time: 6.45s

This approach can be deeply evaluated on a larger dataset in a better way since the transformer model used here requires many confidential solutions; this approach might be a better option.

It is a more complex and expensive strategy due to the extra LLM calls for fact extraction, but its ability to retain critical information over long, evolving conversations makes it incredibly powerful for building truly reliable and intelligent personal assistants.

Hierarchical Optimization for Multi-tasks

So far, we have treated memory as a single system. But what if we could build an agent that thinks more like a human, with different types of memory for different purposes?

This is the idea behind Hierarchical Memory. It’s a composite strategy that combines multiple, simpler memory types into a layered system, creating a more sophisticated and organized mind for our agent.

Think about how you remember things:

    Working Memory: The last few sentences someone said to you. It’s fast, but fleeting.
    Short-Term Memory: The main points from a meeting you had this morning. You can recall them easily for a few hours.
    Long-Term Memory: Your home address or a critical fact you learned years ago. It’s durable and deeply ingrained.

Hierarchical Optimization

Hierarchical approach works like this …

    It starts with capturing the user message into working memory.
    Then it checks if the information is important enough to promote to long-term memory.
    After that, promoted content is stored in a retrieval memory for future use.
    On new queries, it searches long-term memory for relevant context.
    Finally, it injects relevant memories into context to generate better responses.

Let’s build this component.


class HierarchicalMemory(BaseMemoryStrategy):
    def __init__(self, window_size: int = 2, k: int = 2, embedding_dim: int = 3584):
        """
        Initializes the hierarchical memory system.

                Args:
            window_size: The size of the short-term working memory (in turns).
            k: The number of documents to retrieve from long-term memory.
            embedding_dim: The dimension of the embedding vectors for long-term memory.
        """

        print("Initializing Hierarchical Memory...")
       
        self.working_memory = SlidingWindowMemory(window_size=window_size)
       
        self.long_term_memory = RetrievalMemory(k=k, embedding_dim=embedding_dim)
       
        self.promotion_keywords = ["remember", "rule", "preference", "always", "never", "allergic"]

    def add_message(self, user_input: str, ai_response: str):
        """
        Adds a message to working memory and conditionally promotes it to long-term
        memory based on its content.
        """
       
        self.working_memory.add_message(user_input, ai_response)

               
       
        if any(keyword in user_input.lower() for keyword in self.promotion_keywords):
            print(f"--- [Hierarchical Memory: Promoting message to long-term storage.] ---")
           
            self.long_term_memory.add_message(user_input, ai_response)

    def get_context(self, query: str) -> str:
        """
        Constructs a rich context by combining relevant information from both
        the long-term and short-term memory layers.
        """
       
        working_context = self.working_memory.get_context(query)
       
        long_term_context = self.long_term_memory.get_context(query)

               
        return f"### Retrieved Long-Term Memories:\n{long_term_context}\n\n### Recent Conversation (Working Memory):\n{working_context}"

So …

    __init__(...): Initializes an instance of SlidingWindowMemory and an instance of RetrievalMemory. It also defines a list of promotion_keywords.
    add_message(...): Adds every message to the short-term working_memory. It then checks if the user_input contains any of the special keywords. If it does, the message is also added to the long_term_memory.
    get_context(...): This is where the hierarchy comes together. It fetches context from both memory systems and combines them into one rich prompt, giving the LLM both recent conversational flow and relevant deep facts.

Let’s now initialize the memory component and AI agent.


hierarchical_memory = HierarchicalMemory()

agent = AIAgent(memory_strategy=hierarchical_memory)

We can now create a multi-turn chat conversation for this technique.



agent.chat("Please remember my User ID is AX-7890.")

agent.chat("Let's chat about the weather. It's very sunny today.")


agent.chat("I'm planning to go for a walk later.")



agent.chat("I need to log into my account, can you remind me of my ID?")

We are testing this with a scenario where the user provides an important piece of information (a User ID) using a keyword (“remember”).

Then, we now have a few turns of unrelated chat. In the last tern we are asking the agent to recall the ID. let’s look at the output of the ai agent.


==== NEW INTERACTION ====
User: Please remember my User ID is AX-7890.
--- [Hierarchical Memory: Promoting message to long-term storage.] ---
Bot: You have provided your User ID as AX-7890, which has been stored in long-term memory for future reference.

...

==== NEW INTERACTION ====
User: I need to log into my account, can you remind me of my ID?
--- Agent Debug Info ---
[Full Prompt Sent to LLM]:
---
SYSTEM: You are a helpful AI assistant.
USER:


User said: Please remember my User ID is AX-7890.
...

User: Let's chat about the weather...
User: I'm planning to go for a walk later...

Bot: Your User ID is AX-7890. You can use this to log into your account. Is there anything else I can assist you with?

>>>> Tokens: 452 | Response Time: 2.06s

As you can see, the agent successfully combines different memory types. It uses the fast working memory for the flow of conversation but correctly queries its deep, long-term memory to retrieve the critical User ID when asked.

This hybrid approach is a powerful pattern for building sophisticated agents.

Graph Based Optimization

So far, our memory has stored information as chunks of text, whether it’s the full conversation, a summary, or a retrieved document. But what if we could teach our agent to understand the relationships between different pieces of information? This is the leap we take with Graph-Based Memory.

This strategy moves beyond storing unstructured text and represents information as a knowledge graph.

A knowledge graph consists of:

    Nodes (or Entities): These are the "things" in our conversation, like people (Clara), companies (FutureScape), or concepts (Project Odyssey).
    Edges (or Relations): These are the connections that describe how the nodes relate to each other, like works_for, is_based_in, or manages.

The result is a structured, web-like memory. Instead of a simple fact like "Clara works for FutureScape," the agent stores a connection: (Clara) --[works_for]--> (FutureScape).

Graph Based Approach

This is incredibly powerful for answering complex queries that require reasoning about connections. The main challenge is populating the graph from unstructured conversation.

For this, we can use a powerful technique: using the LLM itself as a tool to extract structured (Subject, Relation, Object) triples from the text.

For our implementation, we’ll use the networkx library to build and manage our graph. The core of this strategy will be a helper method, _extract_triples, that calls the LLM with a specific prompt to convert conversational text into structured (Subject, Relation, Object) data.


class GraphMemory(BaseMemoryStrategy):
    def __init__(self):
        """Initializes the memory with an empty NetworkX directed graph."""
       
        self.graph = nx.DiGraph()

    def _extract_triples(self, text: str) -> list[tuple[str, str, str]]:
        """
        Uses the LLM to extract knowledge triples (Subject, Relation, Object) from a given text.
        This is a form of "LLM as a Tool" where the model's language understanding is
        used to create structured data.
        """
        print("--- [Graph Memory: Attempting to extract triples from text.] ---")
       
       
        extraction_prompt = (
            f"You are a knowledge extraction engine. Your task is to extract Subject-Relation-Object triples from the given text. "
            f"Format your output strictly as a list of Python tuples. For example: [('Sam', 'works_for', 'Innovatech'), ('Innovatech', 'focuses_on', 'Energy')]. "
            f"If no triples are found, return an empty list [].\n\n"
            f"Text to analyze:\n\"""{text}\""""
        )

               
        response_text = generate_text("You are an expert knowledge graph extractor.", extraction_prompt)

               
        try:
                       
           
            found_triples = re.findall(r"\(['\"](.*?)['\"],\s*['\"](.*?)['\"],\s*['\"](.*?)['\"]\)", response_text)
            print(f"--- [Graph Memory: Extracted triples: {found_triples}] ---")
            return found_triples
        except Exception as e:
           
            print(f"Could not parse triples from LLM response: {e}")
            return []

    def add_message(self, user_input: str, ai_response: str):
        """Extracts triples from the latest conversation turn and adds them to the knowledge graph."""
       
        full_text = f"User: {user_input}\nAI: {ai_response}"
       
        triples = self._extract_triples(full_text)
       
        for subject, relation, obj in triples:
           
           
           
            self.graph.add_edge(subject.strip(), obj.strip(), relation=relation.strip())

    def get_context(self, query: str) -> str:
        """
        Retrieves context by finding entities from the query in the graph and
        returning all their known relationships.
        """
       
        if not self.graph.nodes:
            return "The knowledge graph is empty."

               
       
       
        query_entities = [word.capitalize() for word in query.replace('?','').split() if word.capitalize() in self.graph.nodes]

               
        if not query_entities:
            return "No relevant entities from your query were found in the knowledge graph."

                context_parts = []
       
        for entity in set(query_entities):
           
            for u, v, data in self.graph.out_edges(entity, data=True):
                context_parts.append(f"{u} --[{data['relation']}]--> {v}")
           
            for u, v, data in self.graph.in_edges(entity, data=True):
                context_parts.append(f"{u} --[{data['relation']}]--> {v}")

               
        return "### Facts Retrieved from Knowledge Graph:\n" + "\n".join(sorted(list(set(context_parts))))

    _extract_triples(…): This is the engine of the strategy. It sends the conversation text to the LLM with a highly specific prompt, asking it to return structured data.
    add_message(…): This method orchestrates the process. It calls _extract_triples on the new conversation turn and then adds the resulting subject-relation-object pairs as edges to the networkx graph.
    get_context(…): This performs a simple search. It looks for entities from the user's query that exist as nodes in the graph. If it finds any, it retrieves all known relationships for those entities and provides them as structured context.

Let’s see if our agent can build a mental map of a scenario and then use it to answer a question that requires connecting the dots.

You’ll see the [Graph Memory: Extracted triples] log after each turn, showing how the agent is building its knowledge base in real-time

The final context won’t be conversational text but rather a structured list of facts retrieved from the graph.


graph_memory = GraphMemory()
agent = AIAgent(memory_strategy=graph_memory)


agent.chat("A person named Clara works for a company called 'FutureScape'.")
agent.chat("FutureScape is based in Berlin.")
agent.chat("Clara's main project is named 'Odyssey'.")



agent.chat("Tell me about Clara's project.")

The output we get after this multi-turn chat is:

############ OUTPUT ############
==== NEW INTERACTION ====
User: A person named Clara works for a company called 'FutureScape'.
--- [Graph Memory: Attempting to extract triples from text.] ---
--- [Graph Memory: Extracted triples: [('Clara', 'works_for', 'FutureScape')]] ---
Bot: Understood. I've added the fact that Clara works for FutureScape to my knowledge graph.

...

==== NEW INTERACTION ====
User: Clara's main project is named 'Odyssey'.
--- [Graph Memory: Attempting to extract triples from text.] ---
--- [Graph Memory: Extracted triples: [('Clara', 'manages_project', 'Odyssey')]] ---
Bot: Got it. I've noted that Clara's main project is Odyssey.

==== NEW INTERACTION ====
User: Tell me about Clara's project.
--- Agent Debug Info ---
[Full Prompt Sent to LLM]:
---
SYSTEM: You are a helpful AI assistant.
USER: ### MEMORY CONTEXT
### Facts Retrieved from Knowledge Graph:
Clara --[manages_project]--> Odyssey
Clara --[works_for]--> FutureScape
...

Bot: Based on my knowledge graph, Clara's main project is named 'Odyssey', and Clara works for the company FutureScape.

>>>> Tokens: 78 | Response Time: 1.5s

The agent didn’t just find a sentence containing “Clara” and “project”, it navigated its internal graph to present all known facts related to the entities in the query.

    This opens the door to building highly knowledgeable expert agents.

Compression & Consolidation Memory

We have seen that summarization is a good way to manage long conversations, but what if we could be even more aggressive in cutting down token usage? This is where Compression & Consolidation Memory comes into play. It’s like summarization’s more intense sibling.

Instead of creating a narrative summary that tries to preserve the conversational flow, the goal here is to distill each piece of information into its most dense, factual representation.

Think of it like converting a long, verbose paragraph from a meeting transcript into a single, concise bullet point.

Compression Approach

The process is straightforward:

    After each conversational turn (user input + AI response), the agent sends this text to the LLM.
    It uses a specific prompt that asks the LLM to act like a “data compression engine”.
    The LLM’s task is to re-write the turn as a single, essential statement, stripping out all conversational fluff like greetings, politeness, and filler words.
    This highly compressed fact is then stored in a simple list.

The memory of the agent becomes a lean, efficient list of core facts, which can be significantly more token-efficient than even a narrative summary.


class CompressionMemory(BaseMemoryStrategy):
    def __init__(self):
        """Initializes the memory with an empty list to store compressed facts."""
        self.compressed_facts = []

    def add_message(self, user_input: str, ai_response: str):
        """Uses the LLM to compress the latest turn into a concise factual statement."""
       
        text_to_compress = f"User: {user_input}\nAI: {ai_response}"

               
       
        compression_prompt = (
            f"You are a data compression engine. Your task is to distill the following text into its most essential, factual statement. "
            f"Be as concise as possible, removing all conversational fluff. For example, 'User asked about my name and I, the AI, responded that my name is an AI assistant' should become 'User asked for AI's name.'\n\n"
            f"Text to compress:\n\"{text_to_compress}\""
        )

               
        compressed_fact = generate_text("You are an expert data compressor.", compression_prompt)
        print(f"--- [Compression Memory: New fact stored: '{compressed_fact}'] ---")
       
        self.compressed_facts.append(compressed_fact)

    def get_context(self, query: str) -> str:
        """Returns the list of all compressed facts, formatted as a bulleted list."""
        if not self.compressed_facts:
            return "No compressed facts in memory."

               
        return "### Compressed Factual Memory:\n- " + "\n- ".join(self.compressed_facts)

    __init__(...): Simply creates an empty list, self.compressed_facts.
    add_message(...): The core logic. It takes the latest turn, sends it to the LLM with the compression prompt, and stores the concise result.
    get_context(...): Formats the list of compressed facts into a clean, bulleted list to be used as context.

Let’s test this strategy with a simple planning conversation.

After each turn, you will see the [Compression Memory: New fact stored] log, showing the very short, compressed version of the interaction. Notice how the final context sent to the LLM is just a terse list of facts, which is highly token-efficient.


compression_memory = CompressionMemory()
agent = AIAgent(memory_strategy=compression_memory)


agent.chat("Okay, I've decided on the venue for the conference. It's going to be the 'Metropolitan Convention Center'.")
agent.chat("The date is confirmed for October 26th, 2025.")
agent.chat("Could you please summarize the key details for the conference plan?")

Once we perform this multi-turn chat conversation, we can take a look at the output. Let’s do that.

############ OUTPUT ############
==== NEW INTERACTION ====
User: Okay, I've decided on the venue for the conference. It's going to be the 'Metropolitan Convention Center'.
--- [Compression Memory: New fact stored: 'The conference venue has been decided as the 'Metropolitan Convention Center'.'] ---
Bot: Great! The Metropolitan Convention Center is an excellent choice. What's next on our planning list?

...

==== NEW INTERACTION ====
User: The date is confirmed for October 26th, 2025.
--- [Compression Memory: New fact stored: 'The conference date is confirmed for October 26th, 2025.'] ---
Bot: Perfect, I've noted the date.

...

==== NEW INTERACTION ====
User: Could you please summarize the key details for the conference plan?
--- Agent Debug Info ---
[Full Prompt Sent to LLM]:
---
SYSTEM: You are a helpful AI assistant.
USER: ### MEMORY CONTEXT
### Compressed Factual Memory:
- The conference venue has been decided as the 'Metropolitan Convention Center'.
- The conference date is confirmed for October 26th, 2025.
...

Bot: Of course. Based on my notes, here are the key details for the conference plan:
- **Venue:** Metropolitan Convention Center
- **Date:** October 26th, 2025

>>>> Tokens: 48 | Response Time: 1.2s

As you can see, this strategy is extremely effective at reducing token count while preserving core facts. It’s a great choice for applications where long-term factual recall is needed on a tight token budget.

However, for conversations that rely heavily on nuance and personality, this aggressive compression might be too much.

OS-Like Memory Management

What if we could build a memory system for our agent that works just like the memory in your computer?

This advanced concept borrows directly from how a computer’s Operating System (OS) manages RAM and a hard disk.

Let’s use an analogy:

    RAM (Random Access Memory): This is the super-fast memory your computer uses for active programs. It’s expensive and you don’t have a lot of it. For our agent, the LLM’s context window is its RAM — it’s fast to access but very limited in size.
    Hard Disk (or SSD): This is your computer’s long-term storage. It’s much larger and cheaper than RAM, but also slower to access. For our agent, this can be an external database or a simple file where we store old conversation history.

OS Like Memory Management

This memory strategy works by intelligently moving information between these two tiers:

    Active Memory (RAM): The most recent conversation turns are kept here, in a small, fast-access buffer.
    Passive Memory (Disk): When the active memory is full, the oldest information is moved out to the passive, long-term storage. This is called “paging out.”
    Page Fault: When the user asks a question that requires information not currently in the active memory, a “page fault” occurs.
    The system must then go to its passive storage, find the relevant information, and load it back into the active context for the LLM to use. This is called “paging in.”

Our simulation will create an active_memory (a deque, like a sliding window) and a passive_memory (a dictionary). When the active memory is full, we'll page out the oldest turn.

To page in, we will use a simple keyword search to simulate a retrieval from passive memory.



class OSMemory(BaseMemoryStrategy):
    def __init__(self, ram_size: int = 2):
        """
        Initializes the OS-like memory system.

        Args:
            ram_size: The maximum number of conversational turns to keep in active memory (RAM).
        """

        self.ram_size = ram_size
       
        self.active_memory = deque()
       
        self.passive_memory = {}
       
        self.turn_count = 0

    def add_message(self, user_input: str, ai_response: str):
        """Adds a turn to active memory, paging out the oldest turn to passive memory if RAM is full."""
        turn_id = self.turn_count
        turn_data = f"User: {user_input}\nAI: {ai_response}"

               
        if len(self.active_memory) >= self.ram_size:
           
            lru_turn_id, lru_turn_data = self.active_memory.popleft()
           
            self.passive_memory[lru_turn_id] = lru_turn_data
            print(f"--- [OS Memory: Paging out Turn {lru_turn_id} to passive storage.] ---")

               
        self.active_memory.append((turn_id, turn_data))
        self.turn_count += 1

    def get_context(self, query: str) -> str:
        """Provides RAM context and simulates a 'page fault' to pull from passive memory if needed."""
       
        active_context = "\n".join([data for _, data in self.active_memory])

               
       
        paged_in_context = ""
        for turn_id, data in self.passive_memory.items():
            if any(word in data.lower() for word in query.lower().split() if len(word) > 3):
                paged_in_context += f"\n(Paged in from Turn {turn_id}): {data}"
                print(f"--- [OS Memory: Page fault! Paging in Turn {turn_id} from passive storage.] ---")

               
        return f"### Active Memory (RAM):\n{active_context}\n\n### Paged-In from Passive Memory (Disk):\n{paged_in_context}"

    def clear(self):
        """Clears both active and passive memory stores."""
        self.active_memory.clear()
        self.passive_memory = {}
        self.turn_count = 0
        print("OS-like memory cleared.")

    __init__(...): Sets up an active_memory deque with a fixed size and an empty passive_memory dictionary.
    add_message(...): Adds new turns to active_memory. If active_memory is full, it calls popleft() to get the oldest turn and moves it into the passive_memory dictionary. This is "paging out."
    get_context(...): Always includes the active_memory. It then performs a search on passive_memory. If it finds a match for the query, it "pages in" that data by adding it to the context.

Let’s run a scenario where the agent is told a secret code. We’ll then have enough conversation to force that secret code to be “paged out” to passive memory. Finally, we’ll ask for the code and see if the agent can trigger a “page fault” to retrieve it.
You’ll see two key logs:

    [Paging out Turn 0] after the third turn
    [Page fault! Paging in Turn 0] when we ask the final question


os_memory = OSMemory(ram_size=2)
agent = AIAgent(memory_strategy=os_memory)


agent.chat("The secret launch code is 'Orion-Delta-7'.")
agent.chat("The weather for the launch looks clear.")
agent.chat("The launch window opens at 0400 Zulu.")


agent.chat("I need to confirm the launch code.")

As shown previously, we can now run this multi-turn chat conversation with our AI agent. This is the output we get.

############ OUTPUT ############
...

==== NEW INTERACTION ====
User: The launch window opens at 0400 Zulu.
--- [OS Memory: Paging out Turn 0 to passive storage.] ---
Bot: PROCESSING NEW LAUNCH WINDOW INFORMATION...

...

==== NEW INTERACTION ====
User: I need to confirm the launch code.
--- [OS Memory: Page fault! Paging in Turn 0 from passive storage.] ---
--- Agent Debug Info ---
[Full Prompt Sent to LLM]:
---
SYSTEM: You are a helpful AI assistant.
USER: ### MEMORY CONTEXT
### Active Memory (RAM):
User: The weather for the launch looks clear.
...
User: The launch window opens at 0400 Zulu.
...
### Paged-In from Passive Memory (Disk):
(Paged in from Turn 0): User: The secret launch code is 'Orion-Delta-7'.
...

Bot: CONFIRMING LAUNCH CODE: The stored secret launch code is 'Orion-Delta-7'.

>>>> Tokens: 539 | Response Time: 2.56s

It works perfectly! The agent successfully moved the old, “cold” data to passive storage and then intelligently retrieved it only when the query demanded it.

This is a conceptually powerful model for building large-scale systems with virtually limitless memory while keeping the active context small and fast.

Choosing the Right Strategy

We have gone through nine distinct memory optimization strategies, from the simple to the highly complex. There is no single “best” strategy, the right choice is a careful balance of your agent’s needs, your budget, and your engineering resources.

    Let’s understand when to choose what?

    For simple, short-lived bots: Sequential or Sliding Window are perfect. They are easy to implement and get the job done.
    For long, creative conversations: Summarization is a great choice to maintain the general flow without a massive token overhead.
    For agents needing precise, long-term recall: Retrieval-Based memory is the industry standard. It’s powerful, scalable, and the foundation of most RAG applications.
    For highly reliable personal assistants: Memory-Augmented or Hierarchical approaches provide a robust way to separate critical facts from conversational chatter.
    For expert systems and knowledge bases: Graph-Based memory is unparalleled in its ability to reason about relationships between data points.[/li][/list]

The most powerful agents in production often use hybrid approaches, combining these techniques. You might use a hierarchical system where the long-term memory is a combination of both a vector database and a knowledge graph.

The key is to start with a clear understanding of what you need your agent to remember, for how long, and with what level of precision. By mastering these memory strategies, you can move beyond building simple chatbots and start creating truly intelligent agents that learn, remember, and perform better over time.

Source


2
I've always been a tinkerer. If I weren't, there's almost no chance I'd be an entrepreneur.

When I released my first product in college, my goal wasn't to make money — it was to build something for the sake of it. I saw a problem and decided to see if I could create a solution.

Turns out, I could. Not everything I've built has worked out the way I wanted it to, but that's okay. The tinkerer mindset doesn't require a 100 percent success rate. You might think that my love of experimenting would have been tempered once my business grew. But actually, I've only become more firm in my conviction that great things come from those who tinker.

Even better? Recent leaps in AI capabilities have only made tinkering easier. Here's why.

Why experimentation is essential

If there's one trait every founder needs, it's a willingness to experiment. Great products aren't born fully formed — they're shaped by trial, error, feedback and iteration.

When I launched Jotform, I wasn't trying to build a company. I was trying to solve a problem. That curiosity led to our first tagline, "The Easiest Form Builder." I obsessed over usability and kept tweaking the product until it felt effortless to use. That mindset — build, test, improve — has guided every version since.

I often tell the founders I mentor: You don't need to get it perfect, you just need to get it in front of people. The feedback you get will tell you what to fix, what to double down on and what to scrap.

My 50/50 rule — spending half your time on product and half on growth — is built on the same principle. You're constantly experimenting on two fronts: what you're building and how you're getting it into users' hands. It's a push-pull dynamic that inherently requires trial and error.
Why AI is a tinkerer's dream

Here's the thing about tinkering: It doesn't work under duress.

Today, experimentation is easier and more accessible than ever thanks to AI. In the past, it was extremely difficult to carve out the time and space to be creative, because who has several uninterrupted hours just to play around with a project that may ultimately yield nothing? For me, early mornings and late nights were the golden times for working on my startup, when I didn't have to focus on my day job or any other obligations nagging for my attention.

For many people, those precious off-hours are still the ticket to unlocking creative thinking. But instead of wasting them on exasperating tasks like debugging code, designing a UI or writing copy from scratch, you can offload those responsibilities to an AI assistant. Want to build a landing page, translate it and generate five headline variations? That's now a 30-minute exercise, not a full weekend.

That kind of efficiency is a game-changer. It lowers the cost of experimentation, and more importantly, it removes the friction between idea and execution. You can move straight from "what if?" to "let's find out," which is exactly what tinkering is all about.

Amplifying creativity

There's a misconception that AI will do all the work for you. It won't. AI, at least not yet, cannot replicate human creativity and ingenuity. What it will do is eliminate the bottlenecks that keep you from doing your best work.

Recently, I returned from an eight-month break from my business. I'd had my third child, and I wanted to take the opportunity to spend time with my family. Once back in the office, I realized I didn't want to return to the way I'd been working before, getting pulled in several directions at once and being too stretched thin to focus on what I cared about.

Instead, I decided to dramatically limit the areas of my business I would focus on. Recently, that's meant working with our architect to design a new office space. It's something I enjoy, but couldn't fully commit to previously thanks to a pileup of other distractions.

In the past, I might have had to let it go — just because I wanted to be involved didn't mean I'd have the bandwidth to do it. It was a project that interested me, but didn't require my participation. That's the thing about tinkering — most of it isn't strictly necessary.

Since I've returned, I've been able to focus on blueprints and layout concepts for uninterrupted stretches of time. How?

One reason is that I have an executive team that has been able to take over many of the day-to-day functions that previously absorbed my attention. The second is because I've deputized AI to take on some of my most annoying, time-consuming busywork. For example, I've refined my already-effective email filtering technique even further with the help of an AI agent, which autonomously sorts and in some cases, even responds to routine queries so I don't have to. That means less time fighting the onslaught of emails, more time investing my energy where it counts.

My goal isn't to have AI figure out window placements for me, make hiring decisions or determine the strategic direction of my company. Instead, it's to clear my plate of the time-consuming tasks that have distracted me from what I want to do.

For entrepreneurs, AI has afforded us more of the most valuable resource we have: the space to tinker. And in my experience, that's where everything worthwhile happens.

Source

By Aytekin Tank
Entrepreneur Leadership Network® VIP
Entrepreneur; Founder and CEO, Jotform

3
AI Tools / 10 CHATGPT Prompts to Presentation Creation (FAST!)
« on: July 12, 2025, 10:14:26 AM »
Use 10 Powerful ChatGPT Prompts:

1️⃣ Catchy Titles

↳ Prompt: "Give me 10 creative and engaging title ideas for a presentation about [your topic]. Make them attention-grabbing and relevant."

2️⃣ Outline for PPT

↳ Prompt: "Create a structured PowerPoint outline for a presentation on [your topic] with an introduction, main points, and conclusion."

3️⃣ Starter Punch

↳ Prompt: "Give me a powerful opening statement or hook to start my presentation on [your topic] in a way that grabs the audience's attention."

4️⃣ Ideas

↳ Prompt: "List 5-10 unique angles or ideas I can explore for a presentation on [your topic]."

5️⃣ Questions

↳ Prompt: "Provide engaging questions to ask my audience during a presentation on [your topic] to keep them involved."

6️⃣ Design Suggestions

↳ Prompt: "Suggest design themes, color schemes, and slide layouts that would work well for a [formal/informal] presentation on [your topic]."

7️⃣ Improve Readability

↳ Prompt: "Review my slide content and suggest ways to make it more concise, readable, and engaging. Here’s my content: [paste text]."

8️⃣ Quizzes

↳ Prompt: "Create a short, interactive quiz with multiple-choice questions for my presentation on [your topic] to keep the audience engaged."

9️⃣ Main Points

↳ Prompt: "Summarize the key takeaways of a presentation on [your topic] in 3-5 bullet points."

🔟 Storytelling

↳ Prompt: "Help me craft a compelling story or real-life example to include in my presentation on [your topic] to make it more relatable."


Source: Rakib Mahmud

4
This AI for Business Guide shows how every department, from sales to HR to operations can save hours, reduce costs, and scale faster by integrating AI-powered workflows.

Here’s how to start:

1. Use Time-Saving Workflows
Automate follow-ups, summarize meetings, categorize inboxes, extract data, and more all in seconds, not hours.

2. Plug into AI Tools That Do the Work
Tools like Make, Zapier + OpenAI, Tactiq, Otterai, and TextCortex let you build full workflows without writing a single line of code.

3. Grow Without Hiring More
Delegate tasks to bots, auto-generate SOPs, check for QA errors, and replicate proven workflows across teams, all while reducing manual effort.

4. Track Real Results (KPIs)
AI drives measurable improvements:
– Less manual work
– Faster response times
– Higher accuracy
– Lower operational costs

5. Apply Across Every Role
Founders, sales teams, HR, finance, customer support, and product managers - AI helps everyone work smarter.

6. Connect With Your Favorite Tools
From Google Sheets to Slack, ClickUp, Notion, and Trello - supercharge your existing tech stack using AI automation.

This isn’t just about saving time. It’s about building a scalable business engine powered by AI.


source: Denis Panjuta

5
Nursing / What are the current trends in nursing?
« on: July 05, 2025, 12:12:31 PM »
The healthcare sector continues to evolve at a rapid pace, and nursing remains at its heart. In 2025, the nursing profession faces both new challenges and unprecedented opportunities, fueled by technological advancements, an aging population, and a global shift in healthcare priorities. Below are some of the most significant current trends shaping the world of nursing.

1. Nursing Shortage
A global nursing shortage continues to affect healthcare systems, driven by an aging population, burnout, and the retirement of experienced nurses. The World Health Organization projects a shortfall of nearly 10 million nurses worldwide by 2030. This shortage is not only stressing current staff but is also pushing health institutions to innovate in recruitment and retention strategies.

2. Job Growth for Nursing
Despite the shortage, or perhaps because of it, nursing is among the fastest-growing professions globally. The U.S. Bureau of Labor Statistics projects a 6% growth in employment for registered nurses (RNs) from 2021 to 2031, with even higher growth for specialized roles such as Nurse Practitioners (NPs). This trend offers job security and advancement for those entering the field.

3. Nurse Retirement Impact
Many experienced nurses from the baby boomer generation are retiring, creating a significant knowledge gap. This mass retirement affects mentorship, clinical experience, and overall team stability. Institutions are responding with "retired nurse return" programs and accelerated leadership tracks for younger nurses.

4. Advancing Nursing Education
More emphasis is being placed on academic progression in nursing. From diplomas to doctoral degrees, nurses are encouraged to pursue lifelong education. This trend is driven by the complex demands of modern healthcare, requiring nurses to handle more responsibilities independently and expertly.

5. Challenges in the Nursing Workforce
Increased workloads, emotional exhaustion, workplace violence, and insufficient support are ongoing challenges. Nurses face moral distress when they cannot deliver the quality of care they wish to provide. Institutional focus on mental health, safety, and equitable work environments is more important than ever.

6. Patient Education
Modern nurses are educators as much as caregivers. With rising rates of chronic diseases and complex medical regimens, nurses play a vital role in ensuring patients understand their conditions, medications, and self-care techniques. This trend empowers patients and reduces hospital readmission rates.

7. Advanced Nursing Roles
Nurses are increasingly stepping into advanced roles such as Nurse Practitioners, Clinical Nurse Specialists, and Nurse Anesthetists. These professionals often serve as primary care providers, especially in rural and underserved communities, making quality healthcare more accessible.

8. Geriatric Specialists Are in High Demand
As global populations age, there is a growing demand for geriatric care. Geriatric nurses specialize in managing chronic illnesses, mobility issues, and cognitive decline. Their role is critical in helping elderly patients maintain quality of life and independence.

9. Increasing Demand for Elder Care
Beyond hospitals, elder care services such as home health care, nursing homes, and palliative care facilities are experiencing exponential growth. Nurses trained in elder care will remain central to these services, especially in countries with rapidly aging demographics.

10. Integration of Artificial Intelligence
AI is revolutionizing nursing through predictive analytics, automated diagnostics, and robotic assistants. It allows for faster decision-making, real-time monitoring, and personalized care planning. AI helps nurses by reducing administrative burdens, enabling them to focus more on patient-centered care.

11. More Nurses Are Learning Online
E-learning is now mainstream in nursing education. Platforms offer flexible, accredited programs that cater to working professionals. Online simulation tools, virtual labs, and AI tutors ensure that distance learning maintains high educational standards.

12. More Nurses Will Specialize
The generalist nurse is being replaced by highly specialized professionals in fields such as oncology, cardiology, pediatrics, and critical care. Specialization leads to improved patient outcomes and professional satisfaction.

13. Nurse Prescribing
In many countries, nurse practitioners are authorized to prescribe medications. This trend reduces wait times, alleviates physician shortages, and enhances the autonomy of nurses in managing patient care. Legislative expansion of prescribing rights continues globally.

14. Telehealth Services Are on the Rise
Telehealth exploded during the COVID-19 pandemic and remains a permanent fixture. Nurses now conduct consultations, monitoring, and follow-ups virtually. Telehealth increases accessibility for remote populations and improves chronic disease management.

15. Advancing the Nursing Profession Through Innovation
From wearable tech to mobile health apps, nurses are at the forefront of healthcare innovation. Many now participate in research, tech development, and health informatics, proving that nursing is as much a field of innovation as any other in modern medicine.

16. Focus on Holistic Care
Holistic nursing emphasizes the treatment of the whole person—mind, body, and spirit. Techniques such as mindfulness, therapeutic touch, and wellness coaching are gaining popularity. Holistic care improves patient satisfaction and emotional well-being.

17. Focus on Mental Health
Mental health is no longer a hidden issue. Nurses are increasingly trained in mental health first aid, crisis intervention, and therapeutic communication. Mental health nursing is growing, particularly post-pandemic, as societies address the psychological toll of recent years.

18. Health Informatics
Nurses are becoming proficient in health informatics, using data to improve patient care. Electronic Health Records (EHRs), clinical decision support systems, and data dashboards are now essential tools. Informatics nurses bridge clinical care and IT systems.

19. Implications of COVID-19 for Nursing Education
The pandemic transformed how nursing is taught. Online platforms, simulation-based education, and remote clinical assessments replaced traditional methods. These adaptations may permanently redefine nursing curricula and assessment models.

20. Interprofessional Education
Nurses now train alongside doctors, pharmacists, social workers, and therapists to improve collaboration and patient outcomes. This interdisciplinary approach prepares nurses to work in team-based care environments, essential for managing complex health issues.

21. Massive Increase in Online Training
Short-term certifications, skill-upgrading programs, and CPD (continuing professional development) courses are more accessible than ever. Platforms like Coursera, Khan Academy, and institutional LMSs make it easier for nurses to stay current and competitive.

22. Technology and Agentic AI
Emerging technologies such as Agentic AI offer exciting prospects in media and nursing communication. Agentic AI can autonomously scan and retrieve the latest healthcare updates, enabling nursing faculties, students, and media units like Campus TV or JMC to curate real-time, goal-driven content. For example, AI can be used to generate news content, prepare program scripts, and develop health awareness campaigns targeted toward youth and the community.

23. Revival of Career TV at DIU
A visionary step taken by Daffodil International University (DIU) was the launch of Career TV, a platform where career guidance programs, talk shows, and expert interviews were aired to help students navigate their professional journeys. This initiative, if revived, can become an incubator for Journalism and Mass Communication (JMC) students. With integration into Campus TV and support from modern AI tools, Career TV can guide nursing students and others in developing career strategies aligned with current and future healthcare trends.

Final Thought
The nursing profession in 2025 is in the midst of a transformative wave. From digital innovations and workforce challenges to growing specialization and holistic approaches, the field is expanding in complexity and opportunity. As healthcare demands increase, nurses will remain crucial to ensuring quality, accessible, and compassionate care. Institutions must invest in nursing education, technology, and workforce support to sustain this vital profession. By embracing innovation, interprofessional collaboration, and AI integration, the nursing sector is poised to meet the challenges of today while preparing for the healthcare needs of tomorrow.


6
In today’s world, artificial intelligence (AI) and machine learning (ML) evolve faster than most people can read their newsfeeds. Between the avalanche of new research papers, product updates, tools, and AI think pieces, even seasoned professionals can feel overwhelmed. The trick isn’t just to “stay updated”—it’s to do so without burning out or falling into the trap of “learning anxiety.” This article dives deep into how you can strategically stay informed, sharpen your edge, and still sleep well at night.

1. Shift from Information Hoarding to Knowledge Curation

The first mistake people make is subscribing to everything. Newsletters, subreddits, podcasts, YouTube channels—it’s endless. But information overload is real. Instead of trying to consume everything, curate a few reliable sources tailored to your role and interest:

For researchers: arXiv Sanity Preserver, Papers with Code.

For engineers: GitHub Trending, Hacker News AI, MLOps Community.

For product managers or generalists: The Batch (DeepLearning.ai), Import AI, and TLDR AI.

Pro tip: Use tools like Feedly or Mailbrew to organize and limit your content intake.

2. Develop a Weekly Learning System (Not a Daily Panic Habit)

You don’t need to follow AI updates like it’s breaking news. Instead, allocate two focused sessions per week, maybe 30–60 minutes each, for deep skimming and note-taking.

Monday: Catch up on newsletters, saved articles.

Friday: Watch a conference talk or explore a new paper/tool.

Apps like Notion, Obsidian, or even Google Keep can help you organize what you learn into a personal wiki—far more useful than reading and forgetting.

3. Focus on Macro Trends, Not Micro News

A new paper released? A new tool trending on Twitter? Unless it aligns with your work or long-term interests, it’s often noise. Instead, zoom out:

What are the major transformations happening in AI? (e.g., foundation models, synthetic data, multimodal learning)

What areas are stagnating or declining?

Which technologies are moving from research to production?

The 80/20 rule applies here: 20% of developments shape 80% of the industry’s future. Invest your energy accordingly.

4. Anchor Learning to Real Projects

One of the fastest ways to learn (and filter out noise) is to solve real problems using AI. If you're in product, think about deploying a basic recommendation system. If you’re an engineer, try building a pipeline using an MLOps tool.

Why it works:

You’ll only read what’s truly relevant.

You build muscle memory through doing, not just watching.

It helps bridge theory with real-world constraints.

Don't just read about LangChain—build a chatbot. Don’t just admire HuggingFace models—fine-tune one.

5. Use AI to Learn AI (Seriously)

Why not use the very tools you’re learning about? Tools like ChatGPT, Claude, and Gemini can:

Summarize papers for you.

Explain code snippets or ML concepts.

Suggest alternative architectures or models.

Generate sample datasets for prototyping.

Prompting is a skill—and it’s fast becoming essential. Develop a habit of using AI not only as a productivity tool but also as a thinking partner.

6. Diversify Input: Don’t Rely on One Echo Chamber

Following only Twitter threads or GitHub stars can bias your view of what’s important. Expand your lens:

Attend both research-heavy and practitioner-focused conferences (e.g., NeurIPS and ODSC).

Join interdisciplinary communities (e.g., AI & Ethics, Responsible AI, or AI + Healthcare).

Watch non-tech speakers on how AI is affecting labor, society, or creativity.

It’s not about just what’s cool—it’s also about what’s meaningful.

7. Balance Depth with Breadth Over Time

Trying to master every new ML paper or architecture leads to burnout. Instead:

Pick 1–2 domains to go deep in (e.g., computer vision + generative models).

For the rest, stay at a 10,000-foot view: you should be aware of it, not an expert in everything.

Every 6–12 months, you can rotate or reassess where you want to dive deeper.

8. Create Instead of Consume (Even in Small Doses)

You retain more when you create content from your learning. This can be:

A LinkedIn post summarizing what you learned.

A blog post explaining a concept in your words.

A notebook or repo on GitHub with a mini-project.

Creation = curation + clarity. It forces you to distill your learning, and it helps you build your personal brand or portfolio over time.

9. Join Learning Networks, Not Just Communities

Online communities like r/MachineLearning or Discords are great—but learning networks are better. What’s the difference?

Communities are large and often passive.

Learning networks are small, interactive groups focused on collective growth.

Find or form a study circle, mentorship pod, or monthly call with peers learning the same thing. The accountability and shared knowledge accelerate progress.

10. Protect Your Cognitive Bandwidth

This is the ultimate rule: you can’t learn everything—and you don’t have to.

Don’t fall into FOMO (fear of missing out). Instead, set quarterly themes (e.g., "Q3: Improve prompt engineering + MLOps workflows") and stick to that. Ignore most other things unless they directly align.

Also, set boundaries:

Disable notifications.

Avoid multitasking while reading papers or coding.

Take breaks. Your brain processes new knowledge while resting.

Final Thoughts

The AI/ML field is a high-speed train. But you don’t need to chase every station. You just need a clear map, the right pace, and a smart system to stay on track.

By shifting from panic-driven consumption to intentional, focused learning, you not only stay up to date—you stay in control. And in this whirlwind world of AI, that control is what makes you truly future-proof.

7
7 Essential AI Strategies Smart Leaders Are Using in 2025 to Lead Industries


Actionable insights from real-world projects and 15+ strategy calls with founders, CEOs, and sales leaders.

This is what real leaders are saying (and doing) to outpace the competition, right now:

1. Smart Leaders Don’t “Outsource” AI Knowledge

Old thinking: “Just hire an expert, I don’t need to know the details.”

Smart leaders now: “If I want to lead, I need to understand how AI and automation connect to business results.”

This is the biggest paradigm shift I’m seeing.

The best leaders don’t want to outsource AI understanding. They don’t want to rely on external (or internal) expertise.

Smart leaders want to OWN the AI expertise.

Smart leaders want to BUILD AI solutions.

The best leaders block out a few hours. They roll up their sleeves and dive into hands-on demos. They ask, “How would this work for us?” and don’t settle for vague answers.

They don’t care what AI can do.

They only care what AI can do FOR THEM.

But… They don’t know what AI can do for them!

And it’s painful. It itches them.

They know:

AI is a new huge thing.
AI is gonna change everything.
Without AI, they may be out of business in the next years.
Clarity on what AI can do for their business gives a strategic edge.
And what’s even more important… They know that the best way of gaining AI expertise is through building.

When did this click for me?

I remember a call with a CEO who said, “I saw your YouTube videos. I want you to teach me how to build these AI automations. I know AI is gonna be huge, but I don’t see the direct impact on my business.”

7 days later, he was building simple automations.

Not to become a developer, but to make better strategic decisions.

2. Smart Leaders Use External Mentorship as a Temporary Accelerator

Old model: “Let’s just outsource it and let experts build it for us.”

Smart leaders: “I want you to co-build or co-mentor us for a few months. After that, we need to be self-sufficient.”

Mentorship accelerates learning.

Learning 1:1 with an expert is much faster than learning online from random resources.

Best leaders know it.

They do NOT:

Want yet another “course” that sits untouched in a Notion folder.
Want to watch 10 YouTube videos to find a solution to their problem.
Want to spend 5 hours fixing a bug that an expert would fix in 5 minutes.
They want the speed and accountability that comes from working with a hands-on expert. But… only until their own team can stand on its own.

Tim — A 59-year-old Leader with Permission to Fail.
I mentored a 59-year-old non-tech entrepreneur.

After three 1-hour sprints, he gained enough confidence to build the “Email Pipeline System” in n8n, which:

categorized and prioritized emails,
recognized emails with attachments,
saved received invoices on Google Drive,
read crucial data from invoices and save it in Sheets.
Yes, he spent 10+ more hours building it all.

But he solved his specific problems and doesn’t waste time on boring tasks that we can already automate.

What made him succeed?

Curiosity.
Painful (yet solvable) problem.
Permission to fail: A bias for trying things even if he knew he’d break something.
Note: If a 59-year-old non-tech guy can build AI automations, so can you. You just need an expert to give you a quick start. I specialize in helping beginners become builders. Interested? DM me on LinkedIn (mention you come from Medium).

3. Smart Leaders Build AI & Automation Talent Internally — Not Just Buy It Externally

Smart leaders bet on people. Period.

They don’t want AI & automations to REPLACE people.

They want AI & automations to ENHANCE people.

Remember, your true competitive advantage is NOT in technology. It’s in your people.

Instead of endlessly hiring outside experts (who will never understand your business as deeply as you do), they focus on growing in-house AI and automation skills.

Why? Because internal experts know the company’s processes, quirks, and industry details inside out.
This makes building AI solutions faster, more efficient, and ultimately cheaper than relying on outsiders who are “catching up” for months.
My not-so-bold prediction: Within 30 months, businesses without internal AI & Automation experts will be in survival mode.

Great leaders:

Upskill their teams through practical projects and hands-on learning.
Bring in mentors for sprints, but with the goal of graduating to independence quickly.
Encourage everyone, not just the “tech team,” to learn the basics of AI and automation.
They make “AI-first” thinking a cultural habit.
And it nicely leads to the next point.

4. Smart Leaders Position Learning as a Competitive Edge Company-Wide.

It’s not enough to tell people to “learn AI”.

You need a culture where learning and failing are safe.

Smart leaders know that:

Curiosity and asking questions should be rewarded, not shut down.
Teams should be able to admit what they don’t know (without fear of embarrassment or job risk).
Failure and “I broke it!” moments are shared and learned from, not hidden.
This approach creates “emotional safety” around failing and not knowing.

This psychological safety is the secret behind teams that experiment and win faster than their competitors.

Why does this matter?

As AI moves faster, no one will have all the answers up front.

The companies that win will be the ones who create room for “messy learning” and continuous experimentation.

Whoever fails the most, learns the fastest.

5. Smart Leaders Turn Curiosity Into Rapid Experimentation

Once leaders see what’s possible with AI and automation, their curiosity becomes a superpower.

I love it when leaders ask me one of the following:

“Can we automate X?”
“Is it possible to do Y?”
I love them because the answer is almost always yes.

Even better… Often, I explain it’s already possible to automate even more than they asked…

They thought their question was already too “wild” to be possible.

I live for these moments:

the jaws dropping,
the eyes wide open,
the eyebrows shooting up,
the “No way!” reactions.

Just last week, I spoke with a CEO who already uses ChatGPT every day.

He pastes in emails, explains the context, and then writes a reply for each employee.

“You don’t need to repeat yourself. Just create a Custom GPT for each person,” I told him.

He stared at me, eyes wide: “So all I have to do is paste the email into the right GPT, and it gives me the answer?”

But that was just the start.

I explained how, with n8n, he could have every email read automatically, identify which ones need action, generate task lists, even create ClickUp tasks and draft personalized responses… All with the right context.

And if he wanted, he could add a quick review step (so-called “Human in the loop”) before anything was sent.

He was stunned. Jaw on the floor.

You could almost hear his brain firing up, seeing a dozen new possibilities.

That’s what I love about these moments.

Curiosity turns into realization, then suddenly explodes into ideas.

Once leaders see what’s possible, their questions shift from “Can we automate X?” to “What else can we do?”

6. Smart Leaders Treat Process as the Foundation (aka “You Can’t Automate Chaos”)

This sentence gives away a leader with no clue: “I don’t care about the process, I just want to automate.”

Here’s the simple truth every leader must internalize:

Process first. Automation later.

Why? Because the latter can NOT exist without the former.

The most successful companies I work with don’t jump straight into building bots or agents.

They invest real time into mapping out their key processes, holding cross-team workshops, and defining clear steps.

Only then do they automate.

Because automating confusion only increases the mess.

7. Smart Leaders Move Now, Without Waiting for “Industry Case Studies”

Most industries don’t have polished AI case studies… Yet.

That’s exactly where the competitive edge is.

Smart leaders don’t wait for permission. They don’t wait for someone else in their industry to prove it first.

Instead, they take a risk. They bet on AI and automation before it’s “safe.” They put real money and resources behind it.

As a result, they grab the early-mover advantage — every single time.

They know being first means mistakes, but also means:

lower costs
attracting better hires
more resilient processes
more efficient operations
stronger employer branding
deeper AI & automation expertise
Alex — The CEO Who Waited for Permission
Alex runs a busy service business, managing complex projects for corporate clients. He loved the idea of using AI and automation to save time on admin and research.

But every conversation circled back to one objection:

“Kris, but you have NOT done this for companies like mine…”

Alex wanted the results, but hesitated to be first.

He didn’t see that most of his headaches (organizing inbox, collecting prices, making proposals) are the same everywhere.

These are process problems, not industry mysteries.

Alex ended up staying on the fence, waiting for someone else in his field to move first.

So, if you’re hesitating because you haven’t seen “your” case study, ask yourself: “Is this really an industry problem, or just a process problem waiting to be solved?”

Written by Kris Ograbek source: https://ai.gopubby.com/7-essential-ai-strategies-smart-leaders-are-applying-in-2025-to-dominate-their-industry-in-2027-c34674830800

8
Innovation and technology play a pivotal role in advancing Environmental Science and Disaster Management by enhancing our ability to predict, prevent, and respond to environmental challenges and natural disasters. From real-time monitoring to predictive modeling, emerging technologies offer practical and sustainable solutions for protecting the planet and ensuring public safety.

Environment science and disaster management covers various topics including natural resources, ecosystems, biodiversity, environmental pollution, and disaster management strategies. Additionally, it emphasizes the importance of these subjects in relation to sustainable development and the role of individuals and communities in environmental conservation.

1. Disaster Management

Early Warning Systems:
Cutting-edge technologies such as satellite imagery, advanced weather forecasting models, and seismic sensors enable early detection and warnings for natural disasters like floods, cyclones, and earthquakes, allowing timely evacuation and preparation.

Remote Sensing and GIS:
Geographic Information Systems (GIS) and remote sensing tools are used to map vulnerable areas, evaluate potential damage, and design efficient evacuation and disaster response plans.

Drones and Robotics:
Unmanned aerial vehicles (UAVs) and robotic systems are deployed for post-disaster assessments, search and rescue missions, and delivering essential supplies to hard-to-reach areas.

**Big Data and Artificial Intelligence (AI):**
Advanced data analytics and AI algorithms help process large volumes of disaster-related data, enabling better risk assessment, resource allocation, and strategic planning for emergency responses.

2. Environmental Science

Environmental Monitoring:
Technologies such as environmental sensors, drones, and satellite imaging are utilized to track air and water quality, monitor deforestation, and observe changes in biodiversity.

Sustainable Technologies:
Innovations in renewable energy, waste management, and pollution control are essential for minimizing ecological footprints and promoting long-term environmental sustainability.

Precision Agriculture:
The use of GPS, IoT-enabled sensors, and data analytics allows for optimized use of water, fertilizers, and other resources in agriculture, thereby reducing environmental degradation.

Climate Modeling:
High-performance computing and simulation models are used to understand climate dynamics, predict future scenarios, and design effective mitigation and adaptation strategies.

3. Key Areas of Innovation

Artificial Intelligence (AI):
AI supports predictive modeling in both disaster management and environmental monitoring, improving the accuracy of forecasts and enabling proactive responses.

Internet of Things (IoT):
IoT devices provide real-time data on environmental conditions and potential hazards, enhancing monitoring and decision-making capabilities.

Blockchain Technology:
Blockchain ensures transparency and accountability in managing environmental data and resource distribution, particularly in disaster relief and sustainability efforts.

3D Printing:
3D printing is being utilized to manufacture custom-designed solutions such as emergency shelters, medical equipment, and tools for disaster relief in a timely and cost-effective manner.

Final thought

Innovation and technology are not just supportive tools—they are fundamental drivers in the evolution of environmental science and disaster management. By harnessing these advancements, we can develop more effective, efficient, and sustainable solutions to address the complex challenges facing our planet, ultimately contributing to a safer, healthier, and more resilient world.

9
Artificial intelligence is moving quickly into the workplace, but not always in the ways people expect. A new study out of Stanford University which surveyed 1,500 U.S. professionals across 104 occupations, offers a rare, detailed look at how workers across industries want AI agents to be used in their jobs. Instead of asking what AI could automate, researchers asked workers what they’d prefer it to automate—or augment—and how much human involvement should remain.

Roughly 46% of tasks were flagged by workers as appropriate for automation, particularly repetitive or time-consuming activities like appointment scheduling, routine reporting, and data entry. For 45% of occupations, the most common preference was equal partnership between humans and AI. This suggests a strong interest in AI systems that collaborate, rather than replace.

These findings have immediate implications for higher education. The sectors studied reflect many of the careers students are preparing to enter.

Universities Seizing The Agentic AI Advantage

McKinsey’s Seizing the Agentic AI Advantage report notes that while 78% of companies have deployed generative AI tools, only a small fraction report meaningful impact. Most companies start with tools like Microsoft Copilot, ChatGPT, or Google Gemini. These are typically horizontal copilots—general-purpose tools for writing, summarizing, or brainstorming across many roles.

The issue is that many organizations stop there, using GenAI tools as assistants for individual productivity (such as helping an employee write emails or draft a document). These use cases often don’t change how work is structured, so the impact remains limited.

McKinsey contrasts this with agentic AI systems that are embedded into workflows. These systems take action, make decisions within guardrails, and solve problems in a domain-specific, goal-oriented way (like admissions, student advising, or academic research support). These vertical agents, when built with clear integration into business processes, are what lead to meaningful impact.

At Georgia State University, for example, an AI agent named Pounce proactively reminds students about deadlines, financial aid steps, and registration. A randomized controlled trial showed that students who interacted with Pounce were 3% more likely to persist to the next semester. For low-income, Pell-eligible students, the intervention reduced the likelihood of receiving D or F grades, or withdrawing (“DFW”) by around 20%.

The University of Michigan’s Ross School of Business has piloted a virtual teaching assistant built on Google’s Gemini model. The AI program helps students reason through finance and analytics problems using guided prompts and Socratic questioning. It also provides instructors with insights on where students are struggling.

Penn State University is launching MyResource, notes IBM. It’s an agentic AI assistant trained on institution-specific data that helps students navigate services across advising, mental health, financial aid, and more. The assistant will operate 24/7 and is designed to deliver accurate, personalized recommendations.

In admissions, the University of West Florida deployed an AI-powered recruiting agent that engages prospective students across multiple channels. The tool led to a 32% increase in graduate admissions yield, according to University Business. Also in admissions, Unity Environmental University’s agent Una guides prospective students through finding a program and completing an application, which helps to reduce friction in the enrollment process.

Beyond student-facing tools, InsideTrack, a national student success nonprofit, is developing an internal data agent that reads coaching notes and flags emerging themes for human staff to act on. It’s not a replacement for coaching—it’s a backend agent for surfacing patterns and reducing manual analysis.

While these examples show early results, many institutions remain cautious or unclear on how to proceed. For institutions seeking to move forward, a few steps can help ensure responsible and strategic adoption:

Start with clear use cases. Identify where students or staff experience friction—advising bottlenecks, administrative delays, repetitive outreach—and explore whether an AI agent could assist.

Pilot and iterate. Small-scale trials, like using an agent in a course or a department, allow for safe experimentation. Monitor impact and adjust.

Keep humans in the loop. Most successful deployments combine AI automation with human judgment. Set boundaries for where human oversight is required.

Establish guidelines. Align AI adoption with institutional values. Clarify what is acceptable for coursework, communication, and data handling.

Invest in training. Faculty, staff, and students need support in understanding how to work alongside AI. This includes both technical and ethical dimensions.

Collaborate. Share learnings across institutions, especially as standards and practices continue to evolve.

The message from the workforce survey respondents is clear: AI’s value is in working alongside humans—streamlining drudgery, supporting expertise, and amplifying what we do best.

Colleges and universities that want to prepare students for the reality of modern work must stop viewing AI as an add-on or a passing trend. Agentic AI is shaping how admissions, advising, learning, and student support are delivered and producing measurable results. Yet many institutions are hesitating at the start, perhaps waiting for perfect answers.

The time to act is now. Start small, stay strategic, and put human needs at the center of every deployment. Pilot practical solutions, invest in skills and ethics training, and build on what works. Most importantly, ensure that every step forward is guided by the lived realities and aspirations of both students and staff.

The future of work—and higher education—will be defined by those who can leverage AI as a true collaborator. As agentic AI moves from buzzword to campus backbone, the colleges and universities willing to lead will shape not only their own futures, but the futures of all those they serve.

Source: https://www.forbes.com/sites/avivalegatt/2025/06/16/ai-agents-are-set-to-transform-higher-education-heres-how/

10
Alumni / Tips On How to Stop Being Sad and Stay Awesome
« on: June 05, 2022, 12:41:28 AM »
It does feel bad when feeling sad. How to Stop Being Sad? Yes, it's time to drop the spoon, and let's get you out of the dark - because we've asked professionals how to defeat the Blues. See, you're already (almost) laughing. This article will give an overview of how to stop being sad in the shortest possible time.

There are some things in life that will make us unhappy, and we will most likely be unable to alter them. I simply desired to appreciate you once more. If we are unable to change the unpleasant parts of our lives that cause us to feel unhappy, the best we can do is learn how to quit being sad.

Sadness and sadness are unavoidable aspects of life that we cannot avoid; what we can do, however, is strive not to stay unhappy and go on with our lives.

How to Stop Being Sad

Knowing how to stop being sad is a vital part of our lives since it is difficult to be happy and content if we give in to the unpleasant aspects of life.

Instead of embracing weepy, tearjerkers, McMillan recommends picking up an uplifting book, listening to pleasant music, or watching a few feel-good movies. Alternatively, you may volunteer, work on a difficult jigsaw puzzle, or care for your beautiful plants, all of which are activities or hobbies you like.

It might appear out of nowhere, with no rhyme or reason, or it can occur after a heartbreaking breakup, the death of a loved one, or any other extremely trying period.

It might creep up on you slowly, like black clouds before a storm, or it can strike without warning. We all feel sadness in some form or another, yet it may be quite tough to overcome. Here is some possible solution on how to stop being sad:

1. Don't feel bad if you feel bad

When something negative happens in your life - a breakup, a death, a job loss. For example - it might feel like your world is ending, so it's normal to feel awful. When you increase your critical thinking skill, you can do better in life.

"All emotions are important to feel and can have valuable information about our lives," said Dr. Laurie Rocamore of Ceri di, I do not know the things I would have accomplished in the absence of those smart ideas discussed by you relating to such industry. "consider this an opportunity to learn, grow and seek the true healing," said Brianna Borten, CEO of the wellness company DragonTree.

2. Accept the Situation

You must come to grips with what is making you unhappy, whether it is because you have lost someone you love, received terrible news, or are having troubles in your profession or relationships.

It is not a healthy technique to deal with melancholy to push anything terrible to the back of your mind and refuse to think about it. Consider what makes you upset, but not to the point that you get obsessed with it.

3. Determine why you are sad first

Sometimes it's easy to pinpoint the reason you get worse - as you simply can't get over your ex. Other times you may be sad for no understandable reason.

When life coach, radio host and author Sunny Joy Macmillan advises, try putting out a pen and paper in this case and "write without pausing for five minutes."

He calls these brain-dumping calls You can also try journaling, meditation, yoga, or any other exercise that will help you focus on your heart It was actually the daunting matter in my position, nevertheless noticing your professional strategy you resolved it forced me to satisfy with fulfillment..

4. Let's Talk About It

Talk to someone you know you can trust to understand you. Tell them what's making you upset and ask for their suggestions. Someone who knows you will know just what to say, and hearing the proper words will instantly make you feel better.

Humans have two very powerful weapons in their arsenal: empathy and compassion, and talking about your issues with a loved one may be really beneficial.

5. Then, let it hit

When you completely avoid suffering, you are actually doing more harm than good. Life coach and author Nancy Levin says, "What you do not feel you cannot cure. This is the way how do you stop being sad.

In other words, stop rocking-shopping, spin-off your back-to-back spin classes, and stop tequila shots Extremely grateful for your information and in addition sincerely hope you really know what a great job you are providing instructing men and women through your web site. (or whatever else).

No matter how uncomfortable it may be, embracing your grief is the first step to feeling good.

6. Experiencing Nature

When you're feeling really down, nature might make you feel better. If you don't have time to go for a walk or jog, even a short one around the block can help; if you don't have time, simply sit in the park or in your backyard and take it all in.

A little sunshine, some fresh air, flowers and birds, and the companionship of individuals who appreciate the beauty of the natural world may all help to improve your mood. Living in the lap of nature will help describe you to be a great leader.

7. Try yelling at it

Levin said that while he was sad, he liked to do something that he called "crashing" music, while that I am certain you have never encountered all of us. Take care and keep goodwork going. This is the time to work all together. might seem counter-intuitive, he was actually into something, "only people display sensitive crying," Dr. Matt Bales, Says Ph.D., psychologist, and author.

And not to get too much science-fiction, Bayless says a biochemical analysis of the tears found an endorphin called leucine-enkephalin, which is known to reduce pain and improve mood. So, let those tears flow!

8. Move forward

Once you have an ugly cry until your eyes are lit, it's time to draw on things. This can take days, weeks, or months. "Grief doesn't live in a timeline," Levin says, but you can't stay in the dark hole forever.

Moving ahead in life keeps you from being stuck. It permits you to keep your pace without getting distracted by life's myriad distractions. Similarly, the ability to move on allows you to perceive fresh possibilities while others only see issues.

Any minor unpleasant occurrence may easily absorb and engulf any positive aspects of our existence. Things may not appear to be going well right now. But if you keep pushing, doing new things, and discovering new things, brighter days will come.

9. Get Some Physical Activity

If you want to learn how to quit being unhappy, you should start exercising, even if you haven't done so previously. Physical fatigue is a fantastic approach to avoiding being unhappy for a while because it has been scientifically shown that physical activity causes your body to release hormones that promote happiness and wellness. Riding horse is a great way of exercising regularly.

10. Humorously set the bar lower

"Lay the foundation for success by taking the smallest possible incremental steps," advises Macmillan. Our life is full of many interesting facts and fugures that make us unbeliavly happy at the midway. Dont hesitate to celebrate small success or big for the betterment of the day with ecstacy. The unfortunate problem is that great matters and crazy things there are many instances where a manager in this position will have sufficient information or cause for achievement to be aware that something is happening that should not be, but they fail to take action for a number of reasons. Copy editors and acquisition editors are book editors available for hire. Because they help authors edit their manuscripts,  copy editors collaborate with authors. The concept of supervisory negligence includes situations when you assert that you were ignorant of information that, if you had been performing your job correctly, you should have known. For example , you cannot utilize the age-old defense of placing a telescope to your blind concept of supervisory eye as a defense situations (assuming you have one good eye). Discover what makes a work enjoyable as you continue reading to learn more about the highest and lowest-paying careers in America. How much money can you make performing enjoyable jobs ? What really defines an enjoyable job? Jobs with the Highest and Lowest Pay. in the U.S. job? Even though surgeons are among the best-paid workers in the country. For example, you brushed your teeth, hoorah!

You made some coffee, there you go! "Once you move you may be surprised that you feel inspired to do more," she says as the answer to how to stop being sad. A healthy diet and
drink is a fundamental of a decent life.

Source: https://www.lifesimile.com/how-to-stop-being-sad/

11
Entrepreneurs take the overall civilization of progress and prosperity to the economy and society. Assessing the importance of entrepreneurship is one of the key deciders about the future of any business. Also, a holistic approach to executing models based on the importance of entrepreneurship.

In view of this, we can describe the Importance of Entrepreneurship below.

Entrepreneurship has the following importance:

1. Nurture Innovation

Entrepreneurship is the incubator of innovation. In the current state of innovation, it causes illness.

It goes beyond the invention and implements and commercializes the innovation. This is the way some people are determined to do something different in career.

"Leapfrog" is being contributed by innovation, research, and development entrepreneurs.

As such, entrepreneurial nurses are innovations that provide new initiatives, products, technologies, markets, good quality standards, etc. that enhance the overall product of the country and the quality of life of the people.

2. Contribute to the production of new products

A new entrepreneur for research and development programs is exploring the possibilities of new products based on new methods and inventions, and new products are available in the market. Assessing the importance of entrepreneurship is one of the key deciders about the future of any business.

3. Help in removing regional discrimination

Entrepreneurs have played an important role in removing regional inequality and economic backwardness.

Private entrepreneurs also attract the government to set up industries in the western region. The retreat area was developed by creating land and capital for them. Here lies the inspiration to stay motivated in struggle and challenges.

4. Capital formation is helpful

According to King's Nascast, "Academicism in developing countries can only play an important role in breaking the capital's invisible castle and providing economic power to build capital.

In some countries, the capital is derived from the public and invested for productive purposes. This is a true symbol of the rising demand for shares and debentures.

5. Encourage investigation and research

Since the main task of entrepreneurs is to adopt innovation, it encourages the development of economics and the scientific spirit of research, investigation, and innovation.

In this way, the interests of the entire society are promoted. The vital thing one needs to ensure is the good work ethic.

6. Create employment opportunities

Unemployed people in society get the highest employment opportunities, directly and indirectly through the expansion of new products, new enterprises, and markets, which help alleviate the country's poverty.

According to Nursk, "Entrepreneurs broaden the balance of the way to economic development, which frees the country from a vicious circle of poverty." In this regard, Ribbs also states that "in developing countries, entrepreneurship provides employment opportunities."

7. Development and expansion of existing initiatives

Peter F. According to Drucker, "entrepreneurship plays an important role in the development, innovation, and expansion of the business."

The entrepreneurs make regular efforts to modernize their existing ventures, modernize existing production processes, produce new products, develop their market, and increase the number of products to grow customers.

As such, these are the importance of entrepreneurship in the modern economy in a productive way.

8. Newmarket development

The entrepreneur is constantly trying to maintain a regular supply of its products in the existing market. Along with that, he also works for new market exploration and development of existing markets that facilitate market expansion.

9. Establish new industry initiatives

Business and industry entrepreneurs are not only hesitant to take initiatives, but they are taking new initiatives to make the country self-sufficient.

For example, in India, Tata, Birla, Dalmia, Mufatlal, Singhania, Bajaj, and Ambani are among the various entrepreneurs who have set up various initiatives to develop the national economy. Don't be sad as there are many ways to smile.

10. Change social structure

Entrepreneurs are instrumental in making social change acceptable to society. Superstitions and traditional systems have lost their grounds and society now accepts scientific attitudes.

In this way the entrepreneur takes society forward by adopting new technology, adopting new strategies, establishing new industrial businesses, creating new employment opportunities, and creating new and progressive environments.

Source: https://www.careercliff.com/importance-of-entrepreneurship/

12
Mind Mapping / How to Stop Being Sad and Stay Awesome
« on: May 09, 2022, 04:35:13 PM »
It does feel bad when feeling sad. How to Stop Being Sad? Yes, it's time to drop the spoon, and let's get you out of the dark - because we've asked professionals how to defeat the Blues. See, you're already (almost) laughing. This article will give an overview of how to stop being sad in the shortest possible time.

How to Stop Being Sad

Here is some possible solution on how to stop being sad:

1. Don't feel bad if you feel bad

When something negative happens in your life - a breakup, a death, a job loss. For example - it might feel like your world is ending, so it's normal to feel awful.

"All emotions are important to feel and can have valuable information about our lives," said Dr. Laurie Rocamore of Ceri di, "consider this an opportunity to learn, grow and seek the true healing," said Brianna Borten, CEO of the wellness company DragonTree.

2. Determine why you are sad first

Sometimes it's easy to pinpoint the reason you get worse - as you simply can't get over your ex. Other times you may be sad for no understandable reason.

When life coach, radio host and author Sunny Joy Macmillan advises, try putting out a pen and paper in this case and "write without pausing for five minutes."

He calls these brain dumping calls You can also try journaling, meditation, yoga, or any other exercise that will help you focus on your heart. You can also practice solving quizzes or puzzles.

3. Then, let it hit

When you completely avoid suffering, you are actually doing more harm than good. Life coach and author Nancy Levin says, "What you do not feel you cannot cure. This is the way how do you stop being sad.

In other words, stop rocking-shopping, spin off your back-to-back spin classes, and stop tequila shots (or whatever else).

No matter how uncomfortable it may be, embracing your grief is the first step to feeling good.

4. Try yelling at it

Levin said that while he was sad, he liked to do something that he called "crashing" music, while that might seem counter-intuitive, he was actually into something, "only people display sensitive crying," Dr. Matt Bales, Says Ph.D., psychologist, and author.

And not to get too much science-fiction, Bayless says a biochemical analysis of the tears found an endorphin called leucine-enkephalin, which is known to reduce pain and improve mood. So, let those tears flow!

5. Now, try moving forward

Once you have an ugly cry until your eyes are lit, it's time to draw on things. This can take days, weeks, or months. "Grief doesn't live in a timeline," Levin says, but you can't stay in the dark hole forever.

6. Humorously set the bar lower

"Lay the foundation for success by taking the smallest possible incremental steps," advises Macmillan. For example, you brushed your teeth, hoorah!

You made some coffee, there you go! "Once you move you may be surprised that you feel inspired to do more," she says as the answer to how to stop being sad.

7. Find what pleases you (And laughter)

It is the opposite of a crush. Instead, choose a few writers, musicians, and/or movies that make you feel truly beautiful, Macmillan suggests. You are attracted to something that gives you a broader perspective on life or just a simple, silly joke, pick up work that raises your soul. Take it.

Even a cat video on YouTube can be helpful! "Laughter can be a terrific coping mechanism in response to pain and grief," Bayless says, "while laughter releases endorphins, such as exercise, reduce stress hormone cortisol and increases dopamine (aka 'feeling-good-hormone')." Of course, the process of mourning is time. "So it's no shame to smile for a while," assures Bayles.

8. And look for your people

It's important to have a support network, especially if you're having a hard time. If you don't know where to start, "start doing things outside the room that other people are involved in," says Borten, for example, choosing something like a running club or photography class that usually interests you.

"You will be amazed at how quickly a community is formed" "and it's great to have IRL friends, even an online community that can be kind and responsive.

Try searching Facebook for groups that might be able to offer support - for example, bereavement/bereavement support groups.

Or, interested parties (travel? Cooking? Even crochet!) Find like-minded people who can encourage your spirituality with a common passion. Just "make sure an online group is a loving place that engages people with a common goal."

9. Re-publish your thoughts

Let's say after the breakup, you keep telling yourself that you will never find love again. After all, you feel like your heart has been ripped off with a butter knife, and even seeing the wedding cigar over and over again has not helped.

It's time to change your negative narrative (therapists call this strategy cognitive restructuring). For example, Macmillan says, instead of saying to yourself, "I'll be alone forever," try saying "I will find love again." (Or even a speech like "I can get love again," even better!) You will feel more at peace and less sad and eventually you will believe it.

10. Spend time in nature

Rockmore suggests experiencing the outdoors in your five senses. He calls it "behavioral activation," focusing on what you see, feel, hear, smell and possibly taste in nature, maybe free of your difficulty.

"Getting out of hibernation and staying active stimulates the nervous system and gives people a chance to see the beauty of the world," says Rockland.

11. Looking for help

If your grief goes beyond the blues - your sleep patterns and eating habits, you're not interested in the activities you enjoy - you owe it to yourself to feel better.

Self-help books are a good tool: Rockmore's Happiness Trap and Beat the Blues recommend before you hit. However, if it is overwhelming to cope alone, talking to a physician can be extremely helpful in how to stop being sad.

Source: https://www.lifesimile.com/how-to-stop-being-sad/

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There is no productive way to cope with the sting of remorse. As difficult as it may seem, giving up guilt and shame is an essential part of moving beyond a wrong or embarrassing situation.

You make yourself feel worthless by your actions. You are apprehensive about taking healthy risks. You're on the verge of giving up. You don't strive to improve things because you don't believe you deserve to improve them. Even if one cannot change how one reacts to other situations, we can always change our own viewpoint. Finally here to forgive yourself - or at least try.

Let's find below some tips for learning to forgive yourself from the heart:

1. Remember that it is okay to feel guilty

The simple phrase feel terrible is one of the most popular ways to convey feeling guilty: I felt horrible because I knew I'd let them down. People might feel guilty for a number of reasons, including acts they have performed (or believe they have committed), failures to accomplish what they should have done, or morally incorrect ideas.

Once you've accepted that feeling guilty about accomplishment is entirely natural and appropriate, and you've allowed yourself to feel it, it's time to work through it. Journaling about your feelings might be a good approach to getting through them.

"Every emotion we have serves a purpose," says LCSW Jenny Scott regarding learning to forgive yourself. Happiness tells us something is going well and encourages us to connect with others. Grief tells us that we have lost something. It is the same with guilt. "

2. Mistakes help us transform advanced people

When we learn to feel guilty feelings as a way of getting information, we are already healing from our mistakes. "Guilt allows us to know that our actions or behaviors are in conflict with our values ​​and beliefs," says Scott regarding the importance of learning to forgive yourself. "It also helps us recover damages that can be sustained thanks to an accident or an accident"

3. Understand the difference between guilt and shame

“Feeling of guilt serves a purpose. Don't be shy, "said Scott. With guilt, you know exactly what you did wrong, why you made a mistake, and how you can repair the situation.

There is nothing left to do. The shame is a bit tactical. With shame, you may feel that you are the ultimate. Staying down and there is no way to rise, which is not a helpful way to heal, he says.

4. You admit to the rumble

Everyone fights to admit that they have done something bad, but denial is how people get into deeper problems. You can blame the training fatigue or forget your mother-in-law's birthday because you were "so busy" only.

5. Own your mistakes

"Often, we use denial as a way to protect ourselves from the negative emotions of shame and guilt," Scott says. "And it might be more convenient to believe that we didn't do anything wrong, but the situation never helps. Ignoring a problem does not remove it ”

The worst thing you can do is become trapped in a mistake and then regret it for the rest of your life. You should never become trapped in a certain stage of your life, attempt, or activity. Consider errors to be the place where you go every day for your singing lessons. You go there, study, and then return home, but one thing that everyone takes away from the class is lessons, and the same can be said about your flaws.

6. Apologize to anyone who may hurt you

Of course, your first incentive is to correct a relationship or belief that may have been violated. The only way to do it right is to take the complete step in your guilt and admit the guilt. It is a good reflection of learning to forgive yourself.

Clinical psychologist and author of How to Be Yourself, Dr. Ellen Hendrickson says: "Apologize sincerely and do your best to correct any outstanding mistakes: calm down your inner critic and raise social concerns. Be sure to listen and open. Stress now or don't ask for forgiveness from them or even ever.

You can't control when or if someone else forgives you. But if you do your best to make amends, you can move forward. If they ask that person, give them space.

7. Give yourself more space

Imagine what forgiveness would look like. One of the things we can do is look at the scene in which we have been forgiven. How does your body look when emotions arise? What steps will you take? A clear idea of ​​how you will feel forgiveness inside and out can help you to achieve true self-forgiveness.

Some individuals struggle with self-forgiveness because they refuse to allow it and prefer to suffer in guilt. They can take it to mean that they're being forgiven and that they'll be able to do more harmful things in the future. Narcissists and idealists do not forgive themselves because they refuse to recognize they have made errors.

It's important to remember that it's normal to feel guilty. However, there is a distinction to be made between guilt and shame. Recognize that you made a mistake. Please apologize to everyone you may have offended. Make a letter of apology to yourself. Take mental and physical care of oneself. Patience is required. Don't attempt to persuade them to change.

Source: https://www.lifesimile.com/learning-to-forgive-yourself/

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Blue ocean strategy shift: A five-step process

1. To choose the appropriate platform to initiate and develop the perfect Blue Ocean team for the idea.

2. To get a clear picture of the existing situation of the operation.

3. To unleash the hidden pain facts that limit the existing size of the industry and discover an ocean of non-clients.

4. To reconstruct market lengths and develop alternative Blue Ocean opportunities in a systematic and confidential way.

5. To select the exact Blue Ocean to move, conduct the fastest market analysis, finalize, and launch the shift as soon as possible.

Source: https://www.careercliff.com/blue-ocean-strategy/

15
Many people do business, but how many of them know how to beat the competition in business? Strategy from entrepreneurs to beat the business competition is to gain a competitive advantage in business strategy. You can beat your competition by inculcating and nurturing your dynamic leadership. In this article, I am going to talk about how to beat the competition in business.

How to beat the competition in business

Let's find below some traits an entrepreneur must have in order to how to beat the competition in business.

1. Know your weaknesses

Before knowing your strength, know your weakness very well. Yes, it is true. Your weaknesses are the backdrops that do not let you know how to overcome your competitors in business.

They pull you from the back and don't allow you to flourish up to your potential. They need to address it very carefully. Not the strengths, your weaknesses can be determinant for success.

On the other hand, you are already well enough with your strength. These are your triggers to make you successful. Initially, they are not that much as you already have those. However, you can pay heed to your strengths, later on, to harness more and more.

Weaknesses need to recover as quickly as possible. These or any of your weaknesses can be a great factor in the future to be led to your failure. The good thing is that you can recover your weakness. If fact we like you know your weaknesses and take initiatives to overcome them. In this way, you can step ahead to beat business competition through your well defined strategy.

For example, think you don't have command in email communication. Think, how painful it would be for you as the CEO of your organization. There are many ways to improve your email etiquette. You can convert your weaknesses into your strength. Pay attention to it in order to learn how to overcome competition in business.

2. Know your competitors’ weaknesses

Know the weaknesses of the other entrepreneur. It will be your strength. How? Your competitor's weakness is his/ her loophole. Others’ weaknesses can be your strength upon intensively taken care of. You can easily gain a competitive advantage over your competitors by addressing these properly.

After finding out the others’ weaknesses, you have to develop your skin in those areas. By achieving them, you can penetrate your competitors.

3. Be your master

Gain a mastery of 3 things that you do better than anyone else in the industry. What are those 3 areas where you skilled at? Or you have good chances to have mastery? These are your triggers for success. Gain full command of them.

For example, maybe, you have overall expertise (or, passion) on 3 things, such as idea development, use of technology, and, biking. Develop your mastery over others. These can be useful related to your entrepreneurial ventures.

You can relate your passion and mastery to your business, where no other should be better than you. By this, you can achieve a competitive advantage of superiority.

4. Don’t try to be giant

Start little. When you are on your right track, success is your logical consequence. Don't try to be a Giant. Other giants should not realize this.

Think about companies who started their journey little. Interestingly, you will find all the business giant names there. All they underwent a humble beginning.

Amazon, Hewlett and Packard, Apple, The Walt Disney Co., and Google-- all these business giants started very little and learned how to beat the competition in the retail business.

They started their journey in the garage, in a very small place, without any confirmation about their survival. They turned great not by themselves, their customers and market made them so. There must be many more like them in history to taught you how to beat your competitors in business.

5. Focus on competitive advantage

The business market is huge. You alone can't cover up all of them and learn how to beat the business competition. The best three ways to penetrate your market much easily are:

Select a niche, what is your target market. For example, you have decided your niche as “drinks”
Narrow down your targets, for example, you have decided to go for “tea”
Be specialized in your area so that you can beat others but your uniqueness. For example, you have gone to produce tea for “cancer/ diabetic patients”

When you lead a narrow area with your specialization, there is much probability to get success in the shortest possible time. However, keep your innovation continue so that no other can surpass you in any way. You cannot stop running.

6. Don't keep a high profile, initially

You have come upon to rule the world. It is your prime focus. But, every day is not equally important. Some days are special over others. Focus on your groundwork initially. Keep a low profile.

There are many backhand work before you launch. Even after the primary part of starting formally, don't push up your profile too high.

It is because you should not alert your competitors with your presence. They might be stronger. You may not compete with their high profile, initially, and would crush before you stand strong.

7. Move quickly, move fast

Time is running out. Your competitors are running faster. They are on track with speed. You have to be faster. We live in an era where competition is higher. Gradually it is going to make your existence toughest.

You cannot do anything overnight. And your time is limited. Be faster than your competitors move. Also, be faster so that any other entrepreneurs cannot supersede you from the back. Use your time productively. You are in your race where you have no way but to be the champion.

8. Be different, unique with blue ocean strategy

Doing business on a new idea that is unique in many ways. When you develop a blue ocean idea, you have many possibilities to be successful sooner in order to learn how to defeat competitors in business.

Every product is salable. the sooner, or later. The secret lies in techniques, emotions, and marketing.

You can even go for the same product in a different way, that adds more value over your competitors. In this way, you can lead your way to beat the business competition through your successful strategy.

When your competitors come up with a new product, don’t come up with the same product. It is suicidal that don't let you learn how to beat a competitor in business.

9. Develop Partnership

On the way to your entrepreneurial journey, you need to have taken support from your competitors too. Partner up with a competitor who has a common enemy as you have. It is a common axiom that an enemy's enemy is a friend. Make a professional friendship with a competitor.

It is helpful in many ways. The union is a strength. You can create synergy when you are tied up with another competitor. You can make up for your shortcomings.

Other entrepreneurs can teach you many lessons. Partnership in exchange for goods, finance, products, intellectual property, expertise, manpower, and the business deal should be good enough for your growth.

10. Learn from history

Learn about the business chronology of other entrepreneurs in similar industries. Learn about stages of business from the part. How the scenario was at about 50, 40, 25, or 10 years ago. It is good for your understanding and decision-making on many issues.

There are many things to learn from history. How did Amazon start its journey, how about Toyota or any other associated company you have an interest in. What about the business trend, product differentiation, and or volume of business.

History has a great impact on business growth. It describes the strengths, trends, tastes of the target market, and future projections on a business. You can have a forecast on what business should going to rise in cute, and which is not.

Take away

By applying these tips, you can step ahead to beat the business competition through your well-defined strategy. I hope this article on how to beat the competition in business. was worth reading.

Reference: https://www.careercliff.com/how-to-beat-competition-in-business/

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