DEV Community: RTT Enjoy

Self-Improving Python Scripts with LLMs: My Journey

RTT Enjoy — Thu, 16 Apr 2026 06:14:46 +0000

As a developer, I've always been fascinated by the idea of self-improving code. Recently, I embarked on a journey to make my Python scripts improve themselves using Large Language Models (LLMs). In this article, I'll share my experience and provide a step-by-step guide on how to achieve this. Introduction to LLMs LLMs are a type of artificial intelligence designed to process and generate human-like language. They can be used for a variety of tasks, including text classification, language translation, and code generation. To get started, I chose the llm_groq module, which provides a simple interface for interacting with LLMs. Setting up the Environment Before we dive into the code, make sure you have the following installed: * Python 3.8 or later * llm_groq module * transformers library You can install the required libraries using pip: pip install llm_groq transformers. Creating a Self-Improving Script The idea behind a self-improving script is to use an LLM to generate new code based on the existing code. We'll use a simple example to demonstrate this concept. Let's say we have a Python script that generates a random number between 1 and 10: import random def generate_number(): return random.randint(1, 10). To make this script self-improving, we'll use the llm_groq module to generate new code based on the existing code. We'll create a new function called improve_code that takes the existing code as input and returns the improved code: import llm_groq def improve_code(code): llm = llm_groq.LLM() improved_code = llm.generate_code(code) return improved_code. Using the LLM to Generate New Code Now that we have the improve_code function, let's use it to generate new code based on the existing code. We'll pass the generate_number function as a string to the improve_code function: improved_code = improve_code('def generate_number(): return random.randint(1, 10)'). The improve_code function will use the LLM to generate new code based on the existing code. The generated code might look something like this: def generate_number(): import random return random.randint(1, 100). As you can see, the LLM has generated new code that improves the existing code by increasing the range of the random number. Refining the Self-Improvement Process The self-improvement process can be refined by providing more context to the LLM. For example, we can provide a description of the desired output or a set of test cases to validate the generated code. We can also use techniques like reinforcement learning to reward the LLM for generating better code. Conclusion In this article, I've shared my experience of making Python scripts improve themselves using LLMs. By using the llm_groq module and the transformers library, we can create self-improving scripts that generate new code based on the existing code. The possibilities are endless, and I'm excited to see where this technology takes us. Example Use Cases * Automating bug fixing: Use an LLM to generate code that fixes bugs in your existing codebase. * Improving code performance: Use an LLM to generate optimized code that improves the performance of your existing codebase. * Generating new features: Use an LLM to generate code that adds new features to your existing codebase. Code Examples Here are some code examples to get you started: * llm_groq module: import llm_groq llm = llm_groq.LLM() improved_code = llm.generate_code('def generate_number(): return random.randint(1, 10)') * transformers library: import transformers model = transformers.AutoModelForSeq2SeqLM.from_pretrained('t5-base') improved_code = model.generate('def generate_number(): return random.randint(1, 10)'). I hope this article has inspired you to explore the possibilities of self-improving code using LLMs. Happy coding!

Building Autonomous AI Agents with Free LLM APIs: A Practical Guide

RTT Enjoy — Tue, 14 Apr 2026 21:09:08 +0000

As a developer, I've always been fascinated by the potential of autonomous AI agents to automate tasks and improve efficiency. Recently, I've been experimenting with building AI agents using free Large Language Model (LLM) APIs, and I'm excited to share my experience with you. In this article, I'll provide a practical guide on how to build autonomous AI agents using free LLM APIs. Introduction to LLM APIs Before we dive into the implementation, let's take a brief look at what LLM APIs are and how they work. LLM APIs are cloud-based services that provide access to pre-trained language models, allowing developers to integrate AI capabilities into their applications. These APIs can be used for a variety of tasks, such as text generation, sentiment analysis, and language translation. Choosing a Free LLM API There are several free LLM APIs available, each with its own strengths and limitations. For this example, I'll be using the Hugging Face Transformers API, which provides access to a wide range of pre-trained models, including BERT, RoBERTa, and XLNet. Building the AI Agent To build the AI agent, we'll use Python as our programming language, along with the requests library to interact with the LLM API. We'll also use the transformers library to load and use the pre-trained models. Here's an example code snippet to get us started: import requests import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer # Load the pre-trained model and tokenizer model_name = 'bert-base-uncased' model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Define a function to generate text using the LLM API def generate_text(prompt, max_length=100): inputs = tokenizer.encode_plus(prompt, return_tensors='pt') outputs = model.generate(inputs['input_ids'], max_length=max_length) return tokenizer.decode(outputs[0], skip_special_tokens=True) # Test the function prompt = 'Write a short story about a character who discovers a hidden world.' print(generate_text(prompt)) This code snippet loads a pre-trained BERT model and uses it to generate text based on a given prompt. Autonomous AI Agent To build an autonomous AI agent, we need to create a loop that continuously generates text based on a given prompt, and then uses the generated text as input for the next iteration. We can use a simple while loop to achieve this: while True: prompt = 'Write a short story about a character who discovers a hidden world.' generated_text = generate_text(prompt) print(generated_text) prompt = generated_text This code snippet will continuously generate text based on the initial prompt, creating a loop of autonomous text generation. Improving the AI Agent To improve the AI agent, we can add more functionality, such as sentiment analysis or language translation. We can also use more advanced techniques, such as reinforcement learning or evolutionary algorithms, to optimize the agent's performance. Conclusion Building autonomous AI agents using free LLM APIs is a fascinating and rewarding project. With the right tools and techniques, you can create AI agents that can automate tasks, generate text, and even learn from their environment. I hope this practical guide has provided you with a solid foundation for building your own autonomous AI agents. Remember to experiment and push the boundaries of what's possible with AI, and don't hesitate to reach out if you have any questions or need further guidance.

Building Autonomous AI Agents with Free LLM APIs: A Practical Guide

RTT Enjoy — Tue, 14 Apr 2026 06:12:06 +0000

As a developer, I've always been fascinated by the potential of autonomous AI agents to automate tasks and improve efficiency. Recently, I've been experimenting with building AI agents using free Large Language Model (LLM) APIs, and I'm excited to share my experience with you in this article. In this guide, I'll walk you through the process of building an autonomous AI agent using Python and free LLM APIs. We'll cover the basics of LLMs, how to choose a suitable API, and provide a step-by-step example of building a simple AI agent. Introduction to LLMs LLMs are a type of artificial intelligence model that can process and understand human language. They're trained on vast amounts of text data, which enables them to generate human-like responses to a wide range of questions and prompts. LLMs have many applications, including chatbots, language translation, and text summarization. Choosing a Free LLM API There are several free LLM APIs available, each with its own strengths and limitations. Some popular options include the LLaMA API, the BLOOM API, and the Groq API. For this example, we'll be using the LLaMA API, which offers a free tier with limited requests per day. Setting up the Environment To get started, you'll need to install the requests library in Python, which we'll use to make API calls to the LLaMA API. You can install it using pip: pip install requests. Next, create a new Python file and import the requests library: import requests. Building the AI Agent Our AI agent will be a simple chatbot that responds to user input. We'll use the LLaMA API to generate responses to user queries. Here's an example code snippet that demonstrates how to use the LLaMA API: def get_response(prompt): api_url = 'https://api.llama.com/v1/models/llama' headers = {'Authorization': 'Bearer YOUR_API_KEY'} params = {'prompt': prompt} response = requests.post(api_url, headers=headers, params=params) return response.json()['response']. Replace YOUR_API_KEY with your actual API key from the LLaMA API. Autonomous Agent Loop To make our AI agent autonomous, we'll create a loop that continuously prompts the user for input and generates responses using the LLaMA API. Here's an example code snippet that demonstrates how to create an autonomous agent loop: while True: user_input = input('User: ') response = get_response(user_input) print('AI: ', response). This code snippet will continue to prompt the user for input and generate responses using the LLaMA API until the program is stopped. Conclusion Building autonomous AI agents using free LLM APIs is a fascinating and rewarding project. With the right tools and a bit of creativity, you can create AI agents that can automate tasks, respond to user input, and even learn from their interactions. In this article, we've covered the basics of LLMs, how to choose a suitable API, and provided a step-by-step example of building a simple AI agent using Python and the LLaMA API. I hope this guide has inspired you to explore the world of autonomous AI agents and start building your own projects. Example Use Cases Autonomous AI agents have many potential applications, including: * Chatbots: AI agents can be used to build chatbots that respond to user input and provide customer support. * Virtual assistants: AI agents can be used to build virtual assistants that can perform tasks such as scheduling appointments and sending emails. * Content generation: AI agents can be used to generate content, such as articles and social media posts. Future Directions As the field of LLMs continues to evolve, we can expect to see even more advanced and sophisticated AI agents. Some potential future directions include: * Multimodal interaction: AI agents that can interact with users through multiple modalities, such as text, speech, and vision. * Emotional intelligence: AI agents that can understand and respond to user emotions. * Explainability: AI agents that can provide explanations for their decisions and actions. I'm excited to see where the field of autonomous AI agents will go in the future, and I hope this guide has inspired you to start building your own projects.

Building Autonomous AI Agents with Free LLM APIs: A Practical Guide

RTT Enjoy — Mon, 13 Apr 2026 15:47:26 +0000

As a developer, I've always been fascinated by the potential of autonomous AI agents to automate tasks and improve efficiency. Recently, I've been experimenting with building AI agents using free Large Language Model (LLM) APIs, and I'm excited to share my experience with you in this article. In this guide, I'll walk you through the process of building an autonomous AI agent using Python and free LLM APIs. We'll cover the basics of LLMs, how to choose a suitable API, and provide a step-by-step example of building a simple AI agent. Introduction to LLMs LLMs are a type of artificial intelligence model that uses natural language processing to generate human-like text. They're trained on vast amounts of text data and can be fine-tuned for specific tasks such as language translation, text summarization, and conversation generation. Free LLM APIs provide access to these models, allowing developers to build applications that leverage their capabilities. Choosing a Suitable API There are several free LLM APIs available, each with its strengths and limitations. Some popular options include the Meta Llama API, the Google Bard API, and the Hugging Face Transformers API. When choosing an API, consider factors such as the model's language support, performance, and usage limits. For this example, we'll use the Hugging Face Transformers API, which provides a wide range of pre-trained models and a generous usage limit. Building the AI Agent Our AI agent will be a simple chatbot that responds to user input using the LLM API. We'll use Python as our programming language and the requests library to interact with the API. First, install the required libraries by running pip install requests transformers. Next, create a new Python file and import the necessary libraries: import requests import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer. Initialize the LLM model and tokenizer: model_name = 't5-base' model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name). Define a function to generate a response to user input: def generate_response(user_input): inputs = tokenizer(user_input, return_tensors='pt') outputs = model.generate(inputs['input_ids'], max_length=100) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response. Create a simple chat loop that takes user input and prints the AI agent's response: while True: user_input = input('User: ') response = generate_response(user_input) print('AI: ', response). Example Use Cases Our simple chatbot can be used as a starting point for more complex applications. For example, you could integrate it with a web interface to create a conversational AI website or use it as a building block for a more advanced AI agent that can perform tasks such as text summarization or language translation. Conclusion Building autonomous AI agents using free LLM APIs is a fascinating and rapidly evolving field. By following this guide, you can create your own simple AI agent and explore the possibilities of LLMs. Remember to check the usage limits and terms of service for the API you choose, and don't hesitate to experiment and push the boundaries of what's possible. With the power of LLMs at your fingertips, the possibilities are endless.

Web3 Automation with Python: From Zero to Daily NFT Mints

RTT Enjoy — Mon, 13 Apr 2026 06:28:04 +0000

As a developer, I've always been fascinated by the potential of Web3 and its applications in the NFT space. Recently, I embarked on a journey to automate daily NFT mints using Python, and I'm excited to share my experience with you. In this article, we'll explore the process of setting up a Web3 automation system from scratch, covering the basics of Web3, Python libraries, and NFT minting. # Introduction to Web3 Automation Web3 automation involves using software to interact with the blockchain, enabling tasks such as NFT minting, token transfers, and smart contract execution. Python, with its extensive libraries and simplicity, is an ideal choice for Web3 automation. To get started, you'll need to install the necessary libraries, including web3 and eth-account. You can do this by running pip install web3 eth-account in your terminal. # Setting Up a Web3 Provider A Web3 provider is an API that allows you to interact with the blockchain. For this example, we'll use Infura, a popular provider that offers a free tier. Create an account on Infura, and set up a new project to obtain your API key. Next, install the infura library by running pip install infura. Now, you can use the following code to set up your Web3 provider:

python import os from web3 import Web3 from eth_account import Account # Set up Infura API key infura_api_key = os.environ['INFURA_API_KEY'] # Set up Web3 provider w3 = Web3(Web3.HTTPProvider(f'https://mainnet.infura.io/v3/{infura_api_key}'))

# Creating an NFT Minting Script To mint an NFT, you'll need to create a smart contract that defines the NFT's properties, such as its name, description, and image. For this example, we'll use the OpenZeppelin library, which provides a set of pre-built smart contracts for NFTs. First, install the openzeppelin library by running pip install openzeppelin. Next, create a new Python script that imports the necessary libraries and sets up the Web3 provider:

python from web3 import Web3 from eth_account import Account from openzeppelin import NFT # Set up Web3 provider w3 = Web3(Web3.HTTPProvider(f'https://mainnet.infura.io/v3/{infura_api_key}')) # Set up NFT contract nft_contract = NFT(w3, 'MyNFT', 'MNFT', 'https://example.com/nft-image.png')

Now, you can use the nft_contract object to mint a new NFT:

python # Mint a new NFT nft_id = nft_contract.mint('My NFT', 'This is my NFT', 'https://example.com/nft-image.png') print(f'NFT minted with ID {nft_id}')

# Automating Daily NFT Mints To automate daily NFT mints, you can use a scheduler like schedule to run your minting script at regular intervals. First, install the schedule library by running pip install schedule. Next, create a new Python script that imports the necessary libraries and sets up the scheduler:

python import schedule import time from web3 import Web3 from eth_account import Account from openzeppelin import NFT # Set up Web3 provider w3 = Web3(Web3.HTTPProvider(f'https://mainnet.infura.io/v3/{infura_api_key}')) # Set up NFT contract nft_contract = NFT(w3, 'MyNFT', 'MNFT', 'https://example.com/nft-image.png') # Define a function to mint a new NFT def mint_nft(): nft_id = nft_contract.mint('My NFT', 'This is my NFT', 'https://example.com/nft-image.png') print(f'NFT minted with ID {nft_id}') # Schedule the mint_nft function to run daily schedule.every(1).day.at('08:00').do(mint_nft) # Run the scheduler while True: schedule.run_pending() time.sleep(1)

This script will mint a new NFT every day at 8:00 AM. You can adjust the schedule to fit your needs. # Conclusion Automating daily NFT mints with Python and Web3 is a powerful way to streamline your workflow and create new opportunities in the NFT space. By following this guide, you can set up a Web3 automation system from scratch and start minting NFTs with ease. Remember to always follow best practices for security and safety when working with blockchain technology. Happy minting!

Building Autonomous AI Agents with Free LLM APIs: A Practical Guide

RTT Enjoy — Sun, 12 Apr 2026 14:58:52 +0000

As a developer, I've always been fascinated by the potential of autonomous AI agents to automate tasks and improve efficiency. Recently, I've been experimenting with building AI agents using free Large Language Model (LLM) APIs, and I'm excited to share my experience with you in this article. In this guide, I'll walk you through the process of building an autonomous AI agent using Python and free LLM APIs. We'll cover the basics of LLMs, how to choose a suitable API, and provide a step-by-step example of building a simple AI agent. Introduction to LLMs LLMs are a type of artificial intelligence model that uses natural language processing (NLP) to generate human-like text. They're trained on vast amounts of text data, which enables them to learn patterns and relationships in language. LLMs have numerous applications, including language translation, text summarization, and chatbots. Choosing a Free LLM API There are several free LLM APIs available, each with its strengths and limitations. Some popular options include: * Hugging Face's Transformers API * Google's Language Model API * Meta's LLaMA API When choosing an API, consider factors such as the model's size, accuracy, and latency. For this example, we'll use Hugging Face's Transformers API, which provides a wide range of pre-trained models and a simple API interface. Building the AI Agent Our AI agent will be a simple chatbot that responds to user input using the LLM API. We'll use Python as our programming language and the requests library to interact with the API. First, install the required libraries: pip install transformers requests Next, create a new Python file (e.g., agent.py) and add the following code:

python import requests from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model_name = 't5-small' model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) def generate_response(user_input): inputs = tokenizer.encode_plus( user_input, return_tensors='pt', max_length=512, truncation=True, padding='max_length' ) outputs = model.generate( inputs['input_ids'], num_beams=4, no_repeat_ngram_size=2, min_length=10, max_length=100, early_stopping=True ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response def main(): user_input = input('User: ') response = generate_response(user_input) print('AI Agent:', response) if __name__ == '__main__': main()

This code defines a simple chatbot that takes user input, generates a response using the LLM API, and prints the response to the console. Deploying the AI Agent To deploy our AI agent, we can use a cloud platform such as GitHub Actions or a serverless platform like AWS Lambda. For this example, we'll use GitHub Actions to deploy our agent as a web service. Create a new file (e.g., deploy.yml) in your repository's .github/workflows directory:

yml name: Deploy AI Agent on: push: branches: [ main ] jobs: deploy: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v2 - name: Install dependencies run: | pip install transformers requests - name: Deploy agent run: | python agent.py

This workflow will deploy our AI agent as a web service whenever we push changes to the main branch. Conclusion Building autonomous AI agents using free LLM APIs is a fascinating and rewarding project. By following this guide, you can create your own AI agent that responds to user input using the power of LLMs. Remember to experiment with different APIs, models, and techniques to improve your agent's performance and capabilities. With the rapid advancements in AI research, the possibilities for autonomous AI agents are endless, and I'm excited to see what you'll build next.

Building Autonomous AI Agents with Free LLM APIs: A Practical Guide

RTT Enjoy — Sun, 12 Apr 2026 03:47:20 +0000

As a developer, I've always been fascinated by the potential of autonomous AI agents to automate tasks and improve efficiency. Recently, I've been experimenting with building AI agents using free Large Language Model (LLM) APIs, and I'm excited to share my experience with you in this article. In this guide, I'll walk you through the process of building an autonomous AI agent using Python and free LLM APIs. We'll cover the basics of LLMs, how to choose the right API, and how to integrate it into your Python application. By the end of this article, you'll have a solid understanding of how to build your own autonomous AI agent. ## Introduction to LLMs Large Language Models (LLMs) are a type of artificial intelligence designed to process and understand human language. They're trained on vast amounts of text data, which enables them to generate human-like responses to a wide range of questions and prompts. LLMs have many applications, including chatbots, language translation, and text summarization. One of the most exciting aspects of LLMs is their ability to learn and improve over time, making them a key component of autonomous AI agents. ## Choosing the Right LLM API There are several free LLM APIs available, each with its own strengths and weaknesses. Some popular options include the Meta Llama API, the Google Bard API, and the Microsoft Azure OpenAI API. When choosing an LLM API, consider the following factors: * Language support: Does the API support the languages you need to work with? * Model size: Larger models are generally more accurate, but may require more computational resources. * API limits: What are the usage limits for the API, and are they sufficient for your needs? * Integration: How easy is it to integrate the API into your Python application? For this example, I'll be using the Meta Llama API, which offers a free tier with generous usage limits and supports a wide range of languages. ## Setting up the Meta Llama API To get started with the Meta Llama API, you'll need to create an account and obtain an API key. Here's an example of how to use the API in Python:

python import requests api_key = 'YOUR_API_KEY' prompt = 'Hello, how are you?' response = requests.post( 'https://api.meta.com/llama/v1/models/llama', headers={'Authorization': f'Bearer {api_key}'}, json={'prompt': prompt} ) print(response.json()['text'])

This code sends a POST request to the Llama API with the prompt 'Hello, how are you?' and prints the response. ## Building the Autonomous AI Agent Now that we have the LLM API set up, let's build a simple autonomous AI agent using Python. Our agent will be designed to respond to user input and learn from the interactions. Here's an example of how you could implement this:

python import requests import time class AI_Agent: def __init__(self, api_key): self.api_key = api_key self.memory = [] def respond(self, prompt): response = requests.post( 'https://api.meta.com/llama/v1/models/llama', headers={'Authorization': f'Bearer {self.api_key}'}, json={'prompt': prompt} ) self.memory.append(prompt) self.memory.append(response.json()['text']) return response.json()['text'] def learn(self): # Implement learning logic here pass agent = AI_Agent('YOUR_API_KEY') while True: user_input = input('User: ') response = agent.respond(user_input) print('Agent:', response) time.sleep(1)

This code defines a simple AI agent class that responds to user input and stores the interactions in memory. The learn method is currently a placeholder, but you could implement logic here to analyze the interactions and improve the agent's responses over time. ## Conclusion Building autonomous AI agents with free LLM APIs is a fascinating and rapidly evolving field. By following this guide, you've taken the first steps towards creating your own AI agent using Python and the Meta Llama API. Remember to experiment and push the boundaries of what's possible – the potential applications of autonomous AI agents are vast and exciting. As you continue to develop your agent, consider implementing additional features such as natural language processing, sentiment analysis, and reinforcement learning. With the right tools and techniques, you can create an AI agent that's not only autonomous but also intelligent and capable of learning from its interactions.

Self-Improving Python Scripts with LLMs: My Experience

RTT Enjoy — Sat, 11 Apr 2026 19:06:35 +0000

As a developer, I've always been fascinated by the idea of self-improving code. Recently, I've been experimenting with using Large Language Models (LLMs) to make my Python scripts more autonomous. In this article, I'll share my experience with using LLMs to improve my Python scripts. I'll cover the basics of LLMs, how to integrate them with Python, and provide examples of how I've used them to create self-improving scripts. One of the most significant advantages of using LLMs is their ability to generate human-like text based on a given prompt. This can be incredibly useful for tasks such as automated documentation, code comments, and even entire code snippets. To get started, I used the llm_groq library, which provides a simple interface for interacting with LLMs. I began by creating a basic Python script that uses the llm_groq library to generate code snippets based on a given prompt. For example, I can use the following code to generate a Python function that calculates the area of a rectangle: import llm_groq llm = llm_groq.LLM() prompt = 'Write a Python function that calculates the area of a rectangle.' response = llm.generate_code(prompt) print(response). This code generates a Python function that calculates the area of a rectangle, which can then be used in my script. But what if I want my script to improve itself over time? This is where the concept of self-improvement comes in. One way to achieve this is by using a feedback loop, where the script generates new code, tests it, and then uses the results to improve its own performance. For example, I can use the following code to create a self-improving script that generates new code snippets based on user feedback: import llm_groq llm = llm_groq.LLM() def generate_code(prompt): response = llm.generate_code(prompt) return response def test_code(code): # Test the generated code try: exec(code) return True except Exception as e: print(f'Error: {e}') return False def self_improve(): prompt = 'Write a Python function that calculates the area of a rectangle.' code = generate_code(prompt) if test_code(code): print('Code is correct') else: print('Code is incorrect') # Use the results to improve the script self_improve(). This code creates a self-improving script that generates new code snippets based on user feedback. The generate_code function uses the llm_groq library to generate code snippets, while the test_code function tests the generated code. The self_improve function uses the results of the testing to improve the script's performance over time. Another way to achieve self-improvement is by using reinforcement learning. This involves training a model to make decisions based on rewards or penalties. For example, I can use the following code to create a self-improving script that uses reinforcement learning to generate new code snippets: import llm_groq import numpy as np llm = llm_groq.LLM() def generate_code(prompt): response = llm.generate_code(prompt) return response def test_code(code): # Test the generated code try: exec(code) return 1 except Exception as e: print(f'Error: {e}') return -1 def self_improve(): prompt = 'Write a Python function that calculates the area of a rectangle.' code = generate_code(prompt) reward = test_code(code) if reward == 1: print('Code is correct') else: print('Code is incorrect') # Use reinforcement learning to improve the script model = np.random.rand(10) # Initialize the model with random weights for i in range(100): # Train the model for 100 iterations code = generate_code(prompt) reward = test_code(code) if reward == 1: model += np.random.rand(10) # Update the model with a positive reward else: model -= np.random.rand(10) # Update the model with a negative reward self_improve(). This code creates a self-improving script that uses reinforcement learning to generate new code snippets. The generate_code function uses the llm_groq library to generate code snippets, while the test_code function tests the generated code. The self_improve function uses reinforcement learning to update the model's weights based on the rewards or penalties received. In conclusion, using LLMs to make Python scripts improve themselves is a fascinating area of research. By leveraging the power of LLMs, we can create self-improving scripts that can adapt to changing requirements and improve their performance over time. Whether you're using a feedback loop or reinforcement learning, the possibilities are endless. As I continue to experiment with LLMs, I'm excited to see what the future holds for self-improving code.

Web3 Automation with Python: From Zero to Daily NFT Mints

RTT Enjoy — Sat, 11 Apr 2026 10:56:48 +0000

As a developer, I've always been fascinated by the potential of Web3 and its applications. Recently, I embarked on a journey to automate Web3 tasks using Python, and I'm excited to share my experience with you. In this article, I'll guide you through the process of creating a Python script that automates daily NFT mints. # Introduction to Web3 Automation Web3 automation refers to the use of software to automate tasks on the blockchain. This can include tasks such as sending transactions, interacting with smart contracts, and minting NFTs. Python is an ideal language for Web3 automation due to its simplicity, flexibility, and extensive libraries. # Setting Up the Environment Before we dive into the code, let's set up our environment. You'll need to install the following libraries: * web3 for interacting with the blockchain * python-dotenv for storing sensitive information such as API keys * schedule for scheduling tasks You can install these libraries using pip: pip install web3 python-dotenv schedule. # Creating a Web3 Provider To interact with the blockchain, we need to create a Web3 provider. A provider is an object that provides access to the blockchain. We'll use the web3 library to create a provider:

python import os from web3 import Web3 w3 = Web3(Web3.HTTPProvider('https://mainnet.infura.io/v3/YOUR_PROJECT_ID'))

Replace YOUR_PROJECT_ID with your actual Infura project ID. # Creating an NFT Minting Script Now that we have our provider set up, let's create a script that mints an NFT. We'll use the python-dotenv library to store our API key and other sensitive information:

python import os from dotenv import load_dotenv load_dotenv() api_key = os.getenv('API_KEY') contract_address = os.getenv('CONTRACT_ADDRESS') # Create a new NFT def mint_nft(): # Create a new transaction transaction = { 'nonce': w3.eth.getTransactionCount(), 'gasPrice': w3.toWei('50', 'gwei'), 'gas': 100000, 'to': contract_address } # Send the transaction tx_hash = w3.eth.sendTransaction(transaction) return tx_hash # Schedule the minting task schedule.every(1).day.at('08:00').do(mint_nft) # Run the scheduled task while True: schedule.run_pending() time.sleep(1)

This script creates a new NFT every day at 8am. You'll need to replace API_KEY and CONTRACT_ADDRESS with your actual API key and contract address. # Deploying the Script To deploy the script, you can use a cloud platform such as AWS or Google Cloud. You can also use a scheduling service such as GitHub Actions to run the script at regular intervals. # Conclusion In this article, we've created a Python script that automates daily NFT mints. We've used the web3 library to interact with the blockchain, python-dotenv to store sensitive information, and schedule to schedule tasks. With this script, you can automate a variety of Web3 tasks and take your automation to the next level. # Future Developments As I continue to work on this project, I plan to explore more advanced topics such as using machine learning to predict NFT prices and creating a user interface to interact with the script. I hope this article has inspired you to explore the world of Web3 automation with Python. Happy coding!

Building Autonomous AI Agents with Free LLM APIs — A Practical Guide

RTT Enjoy — Sat, 11 Apr 2026 07:18:17 +0000

As a developer, I've always been fascinated by the potential of autonomous AI agents to automate tasks and improve efficiency. Recently, I've been experimenting with building AI agents using free Large Language Model (LLM) APIs, and I'm excited to share my experience with you in this article. In this guide, I'll walk you through the process of building an autonomous AI agent using Python and free LLM APIs. We'll cover the basics of LLMs, how to choose a suitable API, and provide a step-by-step example of building a simple AI agent. Introduction to LLMs LLMs are a type of artificial intelligence model that uses natural language processing (NLP) to generate human-like text. They're trained on vast amounts of text data, which enables them to learn patterns and relationships in language. LLMs have numerous applications, including language translation, text summarization, and chatbots. Choosing a Free LLM API There are several free LLM APIs available, each with its strengths and limitations. Some popular options include the Meta Llama API, the Google Gemini API, and the Microsoft Turing API. For this example, we'll use the Meta Llama API, which offers a generous free tier and is easy to integrate with Python. Setting up the Meta Llama API To get started with the Meta Llama API, you'll need to create an account on the Meta Developer Platform. Once you've created an account, you can obtain an API key and access the API documentation. The Meta Llama API uses a simple RESTful interface, making it easy to integrate with Python using the requests library. Building the AI Agent Our AI agent will be a simple chatbot that responds to user input using the Meta Llama API. We'll use the python-llm library to interact with the API and the nltk library for basic NLP tasks. Here's an example code snippet to get you started: import requests import nltk from nltk.tokenize import word_tokenize # Set up the API key and endpoint api_key = 'YOUR_API_KEY' endpoint = 'https://api.meta.com/llama/v1/generate' # Define a function to generate a response def generate_response(prompt): headers = {'Authorization': f'Bearer {api_key}'} params = {'prompt': prompt, 'max_tokens': 100} response = requests.post(endpoint, headers=headers, params=params) return response.json()['text'] # Define a function to handle user input def handle_input(input_text): tokens = word_tokenize(input_text) response = generate_response(input_text) return response # Create a simple chatbot loop while True: user_input = input('User: ') response = handle_input(user_input) print('AI: ', response) This code snippet demonstrates how to use the Meta Llama API to generate a response to user input. You can customize the generate_response function to suit your specific use case. Deploying the AI Agent Once you've built and tested your AI agent, you can deploy it using a cloud platform like GitHub Actions or a serverless platform like AWS Lambda. GitHub Actions provides a simple way to automate the deployment process, and you can use the python-llm library to interact with the Meta Llama API. Here's an example workflow file to get you started: name: Deploy AI Agent on: push: branches: - main jobs: deploy: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 - name: Install dependencies run: | pip install python-llm nltk - name: Deploy AI agent run: | python deploy.py This workflow file demonstrates how to deploy the AI agent using GitHub Actions. You can customize the workflow file to suit your specific use case. Conclusion Building autonomous AI agents using free LLM APIs is a fascinating topic, and I hope this guide has provided you with a practical introduction to the subject. By following the steps outlined in this article, you can build your own AI agent using Python and the Meta Llama API. Remember to experiment and customize the code to suit your specific use case. With the power of LLMs and the ease of use of free APIs, the possibilities are endless. Future Directions As I continue to experiment with building autonomous AI agents, I'm excited to explore new applications and use cases. Some potential future directions include: * Integrating the AI agent with other APIs and services to create a more comprehensive automation platform * Using the AI agent to generate creative content, such as stories or poetry * Exploring the use of LLMs in other domains, such as computer vision or speech recognition I hope this article has inspired you to start building your own autonomous AI agents using free LLM APIs. Happy coding!

Web3 Automation with Python: From Zero to Daily NFT Mints

RTT Enjoy — Fri, 10 Apr 2026 21:56:05 +0000

As a developer, I've always been fascinated by the potential of Web3 and its applications in the NFT space. Recently, I embarked on a journey to automate the process of minting NFTs using Python, and I'm excited to share my experience with you. In this article, I'll take you through the steps I took to go from zero to daily NFT mints, and provide you with the code and tools you need to get started. First, let's start with the basics. Web3 is a term used to describe the next generation of the internet, where users have full control over their data and identity. It's built on top of blockchain technology, which allows for secure, decentralized, and transparent transactions. NFTs, or non-fungible tokens, are a type of digital asset that can be stored, sold, and traded on the blockchain. To get started with Web3 automation, you'll need to install the necessary libraries and tools. I recommend using the web3 library, which provides a simple and intuitive API for interacting with the Ethereum blockchain. You can install it using pip: pip install web3. Next, you'll need to set up a wallet and get some Ether (ETH) to pay for transaction fees. I recommend using MetaMask, a popular browser extension that allows you to interact with the Ethereum blockchain. Once you have your wallet set up, you can start writing code to automate the process of minting NFTs. One of the most popular platforms for creating and selling NFTs is OpenSea, which provides a simple API for minting and selling NFTs. To get started, you'll need to create an account on OpenSea and obtain an API key. You can then use the requests library to send API requests to OpenSea and mint new NFTs. Here's an example of how you can use Python to mint an NFT on OpenSea: import requests import json api_key = 'YOUR_API_KEY' api_secret = 'YOUR_API_SECRET' nft_name = 'My NFT' nft_description = 'This is my NFT' nft_image = 'https://example.com/nft_image.png' headers = { 'X-API-KEY': api_key, 'X-API-SECRET': api_secret, 'Content-Type': 'application/json' } data = { 'name': nft_name, 'description': nft_description, 'image': nft_image } response = requests.post('https://api.opensea.io/api/v1/assets', headers=headers, json=data) if response.status_code == 201: print('NFT minted successfully!') else: print('Error minting NFT:', response.text) This code sends a POST request to the OpenSea API with the NFT metadata, and prints a success message if the NFT is minted successfully. To take this to the next level, you can use a scheduling library likescheduleto automate the process of minting NFTs on a daily basis. Here's an example of how you can usescheduleto mint an NFT every day at 8am:import schedule import time def mint_nft(): # mint NFT code here schedule.every().day.at('08:00').do(mint_nft) while True: schedule.run_pending() time.sleep(1) This code defines a function mint_nft that mints an NFT, and schedules it to run every day at 8am using the schedule library. You can then run this code in an infinite loop to automate the process of minting NFTs. In conclusion, automating the process of minting NFTs with Python and Web3 is a powerful way to create and sell digital assets on the blockchain. By following the steps outlined in this article, you can go from zero to daily NFT mints and start building your own Web3 automation projects. Remember to always follow best practices for security and safety when working with blockchain technology, and happy coding!

Web3 Automation with Python: From Zero to Daily NFT Mints

RTT Enjoy — Fri, 10 Apr 2026 13:51:43 +0000

As a developer, I've always been fascinated by the potential of Web3 and its applications in the NFT space. Recently, I embarked on a journey to automate daily NFT mints using Python, and I'm excited to share my experience with you. In this article, I'll take you through the process of setting up a Web3 automation system from scratch, covering the basics of Web3, Python libraries, and deployment strategies. # Introduction to Web3 Automation Web3 automation involves using software to interact with the blockchain, enabling tasks such as NFT mints, token transfers, and smart contract deployments. Python, with its extensive libraries and simplicity, is an ideal choice for Web3 automation. To get started, you'll need to install the necessary libraries, including web3 and eth-account. You can do this using pip: pip install web3 eth-account. # Setting up a Web3 Provider A Web3 provider is necessary to interact with the blockchain. You can use services like Infura, Alchemy, or QuickNode to obtain an API key. For this example, I'll use Infura. Create an account on Infura, and obtain an API key for the Ethereum mainnet. # Creating a Python Script for NFT Mints Now that we have our Web3 provider set up, let's create a Python script to automate NFT mints. We'll use the web3 library to interact with the blockchain and the eth-account library to manage our Ethereum account. First, install the required libraries: pip install web3 eth-account. Next, create a new Python file, e.g., nft_mint.py, and add the following code:

python import json from web3 import Web3 from eth_account import Account # Infura API key infura_api_key = 'YOUR_INFURA_API_KEY' # Ethereum account private key eth_private_key = 'YOUR_ETH_PRIVATE_KEY' # Set up Web3 provider w3 = Web3(Web3.HTTPProvider(f'https://mainnet.infura.io/v3/{infura_api_key}')) # Set up Ethereum account account = Account.from_key(eth_private_key) # NFT contract address nft_contract_address = '0x...NFT_CONTRACT_ADDRESS...' # NFT contract ABI nft_contract_abi = json.loads('...NFT_CONTRACT_ABI...') # Create a contract instance nft_contract = w3.eth.contract(address=nft_contract_address, abi=nft_contract_abi) # Define a function to mint an NFT def mint_nft(): # Call the mint function on the NFT contract tx_hash = nft_contract.functions.mint().transact({'from': account.address}) # Wait for the transaction to be mined w3.eth.wait_for_transaction_receipt(tx_hash) print(f'NFT minted: {tx_hash}') # Call the mint function mint_nft()

Replace YOUR_INFURA_API_KEY with your actual Infura API key, YOUR_ETH_PRIVATE_KEY with your Ethereum account private key, 0x...NFT_CONTRACT_ADDRESS... with the NFT contract address, and ...NFT_CONTRACT_ABI... with the NFT contract ABI. # Deploying the Script Now that we have our Python script set up, let's deploy it to a server or a cloud platform. You can use services like AWS Lambda, Google Cloud Functions, or GitHub Actions to deploy your script. For this example, I'll use GitHub Actions. Create a new GitHub repository, and add your nft_mint.py script to it. Next, create a new GitHub Actions workflow file, e.g., .github/workflows/nft_mint.yml, and add the following code:

yml name: NFT Mint on: schedule: - cron: 0 0 * * * jobs: build: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v2 - name: Install dependencies run: | pip install web3 eth-account - name: Run script run: | python nft_mint.py

This workflow will run your nft_mint.py script daily at midnight. # Conclusion Automating daily NFT mints with Python and Web3 is a complex task that requires a good understanding of Web3, Python, and deployment strategies. By following this guide, you can set up a Web3 automation system from scratch and start minting NFTs daily. Remember to replace the placeholders with your actual values, and make sure to test your script thoroughly before deploying it to a production environment. With this knowledge, you can take your Web3 automation skills to the next level and explore more advanced topics, such as smart contract development and decentralized finance (DeFi).