An Automation Developer is a professional responsible for designing, developing, and implementing automated solutions to streamline processes, increase efficiency, and reduce manual intervention across various domains such as software development, testing, infrastructure management, and business operations. Here’s a detailed description of the role:

1. Understanding of Automation Concepts:

   • Automation Developers possess a strong understanding of automation principles, methodologies, and best practices.
   • They are familiar with automation frameworks, tools, and technologies used for automating repetitive tasks, workflows, and processes.

2. Programming and Scripting Skills:

   • Automation Developers are proficient in programming languages such as Python, Java, C#, JavaScript, and scripting languages like Bash, PowerShell, and Shell Scripting.
   • They use programming and scripting languages to write automation scripts, code automation workflows, and develop custom automation solutions tailored to specific requirements.

3. Automation Frameworks and Tools:

   • Automation Developers have expertise in using automation frameworks and tools such as Selenium, Appium, Robot Framework, Puppet, Chef, Ansible, Jenkins, Travis CI, and GitLab CI/CD.
   • They leverage automation frameworks and tools to build, deploy, and manage automated tests, deployments, configurations, and infrastructure as code (IaC) processes.

4. Continuous Integration and Continuous Deployment (CI/CD):

   • Automation Developers implement CI/CD pipelines and workflows to automate the build, test, and deployment processes of software applications and infrastructure changes.
   • They integrate automated testing, code analysis, code quality checks, and deployment automation into CI/CD pipelines to achieve faster and more reliable software delivery.

5. Test Automation:

   • Automation Developers specialize in test automation by creating automated test scripts, test suites, and test frameworks for functional testing, regression testing, performance testing, and load testing.
   • They use test automation tools and libraries to automate the execution of test cases, validate software functionality, and detect defects early in the development lifecycle.

6. Infrastructure Automation:

   • Automation Developers automate infrastructure provisioning, configuration, deployment, and management using infrastructure as code (IaC) practices.
   • They define infrastructure components, environments, and configurations as code using tools like Terraform, CloudFormation, and Azure Resource Manager (ARM) templates for automated infrastructure deployment and scaling.

7. Process Automation:

   • Automation Developers automate business processes, workflows, and tasks using robotic process automation (RPA) tools, workflow automation platforms, and business process management (BPM) software.
   • They identify repetitive manual tasks, analyze process dependencies, and design automated solutions to optimize resource utilization, reduce errors, and improve productivity.

8. Monitoring and Orchestration:

   • Automation Developers implement automated monitoring, alerting, and orchestration solutions to manage and control automated processes, systems, and workflows.
   • They integrate monitoring tools, event-driven automation, and orchestration engines to monitor system health, trigger automated responses, and ensure system reliability and performance.

9. Security and Compliance Automation:

   • Automation Developers incorporate security and compliance checks into automated workflows and processes to enforce security policies, standards, and regulations.
   • They automate security assessments, vulnerability scanning, access controls, and compliance audits using security automation tools and scripting techniques to mitigate risks and ensure regulatory compliance.

10. Collaboration and Communication:

    • Automation Developers collaborate with cross-functional teams, including developers, testers, operations engineers, and business stakeholders, to identify automation opportunities, gather requirements, and implement automation solutions.
    • They communicate effectively, document automation workflows, provide training and support, and promote knowledge sharing to ensure successful adoption and utilization of automation capabilities within the organization.

In summary, an Automation Developer plays a crucial role in driving digital transformation, improving operational efficiency, and accelerating innovation by leveraging automation technologies to automate processes, tasks, and workflows across software development, testing, infrastructure management, and business operations domains. By combining technical expertise, problem-solving skills, and domain knowledge, they empower organizations to achieve agility, scalability, and competitiveness in today’s dynamic and fast-paced digital landscape.

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Introduction

In Go, the Functional Options pattern is a powerful technique that allows developers to provide flexible and customizable behavior to functions or methods by using functional options as arguments. This pattern is commonly used in Go libraries and frameworks to provide a clean and concise API for users.

What is the Functional Options Pattern?

The Functional Options pattern is a design pattern that leverages the power of higher-order functions and closures in Go to provide a flexible way of configuring objects or functions. Instead of using a large number of parameters or flags, the Functional Options pattern allows developers to pass in a series of functions, each of which configures a specific aspect of the object or function.

The Functional Options Pattern in Go addresses several challenges related to configuration and initialization of objects:

1. Growing Number of Parameters: Traditional function signatures can become unwieldy as the number of parameters increases. The functional options pattern allows you to add new options without changing the function signature or breaking existing code.

2. Readability: When a function takes multiple parameters, especially of the same type, it can be hard to remember the order and meaning of each. With functional options, each option is clearly labeled and self-explanatory, enhancing code readability.

3. Default Values: The functional options pattern allows you to easily provide default values for your options. If an option is not provided when the function is called, the default value is used.

4. Optional Parameters: In some cases, you might want to make some parameters optional. The functional options pattern allows you to do this easily, providing a flexible interface.

5. Encapsulation and Validation: Each option is a function that can contain its own validation logic. This allows you to encapsulate the logic for each option and keep your main function clean and simple.

How does it work?

The Functional Options pattern works by defining a function type that represents an option. This function type takes a pointer to the object or function being configured as its argument and modifies it accordingly. The object or function being configured typically has a corresponding struct type that holds the configuration options as fields.

To use the Functional Options pattern, developers can define a variadic function that takes a series of option functions as arguments. Inside this function, the options are applied one by one to the object or function being configured.

What is variadic function?

A variadic function in Go is a function that can be called with any number of trailing arguments. This means you can pass as many arguments as you want into the variadic function.

The syntax for declaring a variadic function involves using an ellipsis … before the type of the last parameter. The function receives this as a slice of the type.

Here’s an example:

package main

import "fmt"

// This is a variadic function that accepts any number of integers
func sum(nums ...int) {
    fmt.Print(nums, " ")
    total := 0
    for _, num := range nums {
        total += num
    }
    fmt.Println(total)
}

func main() {
    sum(1, 2)
    sum(1, 2, 3)
    nums := []int{1, 2, 3, 4}
    sum(nums...)
}

In this example, sum is a variadic function that takes any number of int arguments. In the main function, we call sum with different numbers of arguments.

Example

Let’s illustrate the Functional Options pattern with an example. Suppose we have a Server struct that represents an HTTP server in Go. We want to provide users with the ability to configure various aspects of the server, such as the port it listens on, the timeout duration, and whether to enable logging.

First, we define the Server struct:

package main

import "fmt"

type Server struct {
    Host     string
    Port     int
    Protocol string
    Timeout  int
}

type ServerOption func(*Server)

func WithHost(host string) ServerOption {
    return func(s *Server) {
        s.Host = host
    }
}

func WithPort(port int) ServerOption {
    return func(s *Server) {
        s.Port = port
    }
}

func WithProtocol(protocol string) ServerOption {
    return func(s *Server) {
        s.Protocol = protocol
    }
}

func WithTimeout(timeout int) ServerOption {
    return func(s *Server) {
        s.Timeout = timeout
    }
}

func NewServer(options ...ServerOption) *Server {
    server := &Server{
        Host:     "localhost",
        Port:     8080,
        Protocol: "http",
        Timeout:  30,
    }

    for _, option := range options {
        option(server)
    }

    return server
}

func main() {
    server := NewServer(
        WithHost("example.com"),
        WithPort(9000),
        WithProtocol("https"),
        WithTimeout(60),
    )

    fmt.Printf("Server: %+vn", server)
}

The post Functional Options Pattern in Go appeared first on ProdSens.live.

Website :- https://civitai.com/ More :- https://tensor.art/

Here is a draft article on generating unlimited AI images for free online:

More Here :- https://codexdindia.blogspot.com/2024/02/generate-unlimited-ai-images-for-free.html

Generate Unlimited AI Images for Free Online

Artificial intelligence (AI) has made it possible to generate stunning images through text prompts. Thanks to recent advances in AI like DALL-E 2, Stable Diffusion, and others, anyone can now create unique AI artworks for free using online tools. In this article, we will explore some of the best free online AI image generators available today.

Civitai

One of the most popular free AI image generators right now is Civitai. Civitai uses a cutting-edge AI model to turn text prompts into photorealistic images in seconds.

To use Civitai, simply go to their website and type a text description of the image you want to generate. For example, you could type “An astronaut riding a horse on Mars”. Hit enter and Civitai will instantly generate a unique AI image based on your prompt.

Civitai offers unlimited free image generation without any logins or accounts required. You can generate as many AI images as you want for personal or commercial use. The interface is easy to use and they provide helpful tips for crafting better prompts.

Tensor.art (Daily 100 Credits)

Tensor.art is a new free web app powered by Anthropic’s Claude AI. It allows you to generate an unlimited number of AI images completely for free.

The interface is simple with a text box to enter your desired prompt. Hit “Generate” and Claude’s AI model will create a unique corresponding image. The results are surprisingly good for a free tool.

Tensor.art also stands out with its helpful prompt engineering tips. As you use it, Claude provides feedback on how to improve your prompts to get better results. Their AI assistant makes it easy for anyone to quickly create quality AI images.

Other Options

Stability AI

Stability AI is another excellent free online tool for AI image generation. They offer a free demo that lets you test drive their Stable Diffusion model.

With the Stability AI demo, you can generate 4 free AI images per day without an account. To access more generations per day, you can create a free account which gives you 25 free credits. Each image costs 1 credit.

The AI image quality from Stable Diffusion is impressive for a free tool. You can create realistic and artistic renders from text prompts across a variety of styles and genres. It’s a great way to explore AI image generation risk-free.

Experiment Risk-Free

Thanks to these free online AI image generators, anyone can now experiment with creating their own unique AI art. Whether you’re an artist, designer, content creator, or just curious – you can play around with generating unlimited AI images without spending a dime.

Start exploring what’s possible by describing your wildest ideas. Keep tweaking prompts until you create something amazing. Have fun unleashing your creativity with these free and easy-to-use AI tools online. The possibilities are endless when you can generate unlimited AI images at no cost.

The post Generate Unlimited AI Images for Free Online appeared first on ProdSens.live.

Intro
React, is one of the most popular frontend framework. Thanks to the great community we can easily read and process data directly from an xls file.
In this guide i will walk you through the steps on how to read data from a spreadsheet and display it on your react application.

Prerequisites
Before you begin make sure you have,
1 Node and npm installed on your system.

Step 1
Install Sheet JS.

npm install xlsx

Step 2
Import Sheet js into your jsx file.

import * as XLSX from 'xlsx';

Step 3
Create a function to handle upload of the xls file.
Start by creating a reader variable using a file reader constructor.

const reader = new FileReader();

Utilize the readAsBinaryString method to initiate the reading process for the specified file from the event argument.

reader.readAsBinaryString(e.target.files[0]);

Once the file has been successfully read, the load event is triggered. Proceed to extract the data following the steps and assign it to a variable.

    reader.onload = (e: any) => {
      const data = e.target.result;
      const workbook = XLSX.read(data, { type: 'binary' });
      const firstSheet = workbook.SheetNames[0];
      const secondSheet = workbook.SheetNames[1];
      const firstSheetData =
       XLSX.utils.sheet_to_json(workbook.Sheets[firstSheet]);
      const secondSheetData =
 XLSX.utils.sheet_to_json(workbook.Sheets[secondSheet]);
  console.log({first_sheet: firstSheetData, second_sheet: secondSheetData})
    };

After following these steps, you should have a function similar to this.

const handleFileUpload = (e)=> {
const reader = new FileReader();
reader.readAsBinaryString(e.target.files[0]);
    reader.onload = (e: any) => {
      const data = e.target.result;
      const workbook = XLSX.read(data, { type: 'binary' });
      const firstSheet = workbook.SheetNames[0];
      const secondSheet = workbook.SheetNames[1];
      const firstSheetData =
       XLSX.utils.sheet_to_json(workbook.Sheets[firstSheet]);
      const secondSheetData =
 XLSX.utils.sheet_to_json(workbook.Sheets[secondSheet]);
  console.log({first_sheet: firstSheetData, second_sheet: secondSheetData})
    };

Leave a like if this helped, Thanks
Happy Coding!

The post How to read XLS Spreadsheets with React js. appeared first on ProdSens.live.

We recently had the pleasure of hosting an AMA that delved into the topic of AI and product. Emily Tate, Managing Director at Mind the Product, was joined by Chris Butler, Group Product Manager – Machine Learning at Google. Watch the video in full, or read on for their key points. Read more »

The post A year with ChatGPT and product innovation: Navigating the AI landscape appeared first on Mind the Product.

The post A year with ChatGPT and product innovation: Navigating the AI landscape appeared first on ProdSens.live.

Follow me on Github

***Introduction:*
With the world of work rapidly changing, remote work has become a norm for many professionals. Whether you’re a remote work newbie or a seasoned pro, optimizing your productivity from the comfort of your own space can be a challenge. In this post, I’ll share 10 practical tips that will help you stay focused and productive while working remotely.

**1. Create a Dedicated Workspace:
Setting up a dedicated workspace that’s comfortable and free from distractions is essential. It signals to your brain that it’s time to work, making it easier to stay on task.

2. Establish a Routine:
A daily routine can provide structure to your day. Set specific work hours, and be consistent in starting and ending your workday at the same time.

3. Dress for Success:
While you might be tempted to work in your pajamas, getting dressed as if you were going to the office can help you get into the right mindset for work.
4. Use Task Management Tools:
Leverage task management apps to organize your work. Tools like Trello, Asana, or Todoist can help you stay on top of tasks and deadlines.
5. Set Clear Goals:
Define your daily and weekly goals to keep yourself motivated. Knowing what you want to achieve each day will make your work more purposeful.

6. Take Regular Breaks:
Sitting for long hours can be counterproductive. Schedule short breaks to stretch, hydrate, or take a walk to refresh your mind and body.

7. Minimize Distractions:
Identify your biggest distractions and find ways to eliminate them. This might mean silencing notifications, creating specific work periods, or using website blockers.
8. Communicate Effectively:
Maintain regular communication with your team and superiors. Use video calls, chats, and emails to stay connected and informed.

9. Learn to Say No:
Overcommitting can lead to burnout. Be realistic about what you can accomplish and don’t be afraid to decline additional tasks when necessary.

10. Reflect and Adapt:
Regularly assess your remote work setup and productivity. Adapt your routine and strategies based on what works best for you.
Conclusion:
Remote work can be incredibly rewarding when you’re productive and balanced. Implementing these 10 tips can help you not only survive but thrive in the remote work environment. Remember, it’s all about finding what works best for you and continuously improving your remote work habits.
Share Your Tips:
What are your favorite tips for effective remote work? Share your insights in the comments below! Let’s build a community of remote workers supporting each other.

#RemoteWork #Productivity #WorkFromHome #RemoteWorkTips

The post Title: 10 Tips for Effective Remote Work Productivity appeared first on ProdSens.live.

Table of Contents

• Introduction
• Overview of AI and machine learning
• Basics of Prompt Engineering
• Definition of Prompt and Prompt Engineering
• Types of prompting
• Tips for Crafting Prompt
• Importance of prompt engineering
• Application of Prompt Engineering In Various fields
• Real-world examples
• Pitfalls to avoid when using prompts
• Ethical considerations in prompt design
• Conclusion

Introduction

With the rapid growth of AI in our society, understanding how to communicate with AI models is essential for improving their output.
By understanding how AI models function and crafting effective prompts, users can extract valuable insights from AI models and solve complex problems innovatively. This guide is designed for a wide audience, including both technical and non-technical users as we delve into enhancing AI model interactions.

Overview of AI and Machine Learning

Artificial Intelligence (AI) is the field of computer science that emulates human intelligence. It is capable of solving problems, mimicking human reasoning and aid decision-making, thereby automating processes and increasing productivity across various domains.

AI models have the capability to understand and process natural language, known as Natural Language Processing (NLP). NLP enables AI models to comprehend, generate, and interact with human language.
These models are trained using vast amounts of data, enabling them to learn and improve their performance over time. For instance, an AI model can recognize everyday objects like a cup because it has been trained on thousands of images of cups. This process of learning from data, known as Machine Learning (ML), is at the core of AI’s adaptability and success.
Examples of AI models include ChatGPT, LLaMA, Claude.

Basics of Prompt Engineering

Prompts are clear, concise instructions used to communicate with AI models and guide them to generate desired outputs. This process of designing and constructing prompts to guide AI models is known as Prompt Engineering.

Types of prompting

• Zero-shot prompting: This involves giving the AI instructions without examples.

• Few-shot prompting: This involves giving the AI instructions with examples.

• Chain-Of-Thought Prompting: This technique involves step by step reasoning in an AI system. It enables you to build upon the output of previous responses in a conversation with an AI model. This technique is especially effective when combined with few-shot prompting, enhancing the quality of responses.

Tips for Crafting Prompts

• Write clear Instructions: Provide the AI model with clear and specific instruction. It can be a short or long prompt, what is more important is to provide all the necessary information required.
  This may include the desired output format, the topic domain, and any relevant constraints.

• Use delimiters: Delimiters are characters that separate specific sections of text within a prompt, enhancing its clarity and effectiveness. Delimiters provide structure and help the AI understand the intended context. Examples of commonly used delimiters include:
  Triple quotes:(‘ ‘ ‘ ‘ ‘ ‘)
  Triple dashes : (- – – – – -)
  Angle brackets: (< >)
  XML tags: ( )

For instance, when providing programming code as a prompt, using triple backticks as delimiters can be effective:

 ```python
def calculate_square(x):
    return x * x

OR

When asking an AI model to write a summary of a book, you could use the following clear instruction with delimiters:

Summarize the book "To Kill a Mockingbird" by Harper Lee. Provide a concise summary in less than 200 words.

• Break Down Complex Tasks: Simplify tasks into smaller steps to aid the AI model’s understanding of the task.

• Adjust Temperature: Temperature controls the randomness of the generated outputs. A low temperature of 0.1 will make the AI model more conservative. A high temperature of 1.0 or above will make the model more creative. High temperature is suitable for creative writing.

• Requesting assistance with prompt formulation: This is a situation where you communicate your query to an AI model and ask for assistance in structuring your prompt to get your desired response.

What's the best prompt for ChatGPT to learn my writing styles and respond to my mails for me ?

• Specify a Persona: This involves instructing the AI to write as if it were adopting a specific character or personality. This approach enhances the AI’s output to match the intended tone and style, ultimately creating more relevant content. For instance, one can request the AI to write professionally for business reports, conversationally for casual exchanges, or even emulate historical figures for a unique style.

Write a letter as if you were Abraham Lincoln, discussing the Civil War.

• Analyze output: Ask the model to check whether conditions are satisfied.
  Critically analyze your previous response, note what can be better and give me feedback.

Prompt Engineering is an iterative process and there are no perfect prompts for an output. Experiment with different phrasings, analyze the AI’s response and refine prompts with examples to achieve desired results.

Importance of Prompt Engineering

• Prompt Engineering allows users to guide AI models for desired outputs, ensuring It promotes accurate and relevant responses.

• Well structured prompts can help reduce biases in AI responses.

• Prompt engineering allows tailoring the model’s output for specific tasks in various domains.

Applications of Prompt Engineering In different career fields

Prompt engineering is not exclusive to software engineers; it is a valuable tool for non-technical users as well. Non-technical users can leverage prompt engineering to interact with AI models and generate outputs without coding.
Here are some real-world examples of how prompt engineering can be used in different fields:

• Marketing: Marketers can use prompt engineering to generate marketing ideas and emails, create ad copy, social media posts, analyze sentiments from customer reviews.

• Customer service: Prompt engineering can be used to create chatbots that can answer customer questions and resolve issues.

• Education: Prompt engineering can be used to create personalized learning experiences and generate educational content like your project

• Content Writing: Prompt engineering can be used for creative writing, to check for grammatical error, spelling and writing tone.

• Technical field: Prompt engineering can be used to generate code, write unit tests, debug code, clean data, extract features, and build machine learning models. AI models can be prompted to translate between different formats such as JSON to HTML, you know, XML, all kinds of things. Markdown.

These are just a few examples of how prompt engineering can be used in different career fields.

Pitfalls to avoid when using prompts

• Avoid using vague or ambiguous prompts, as they can lead to undesired outputs.

• Never rely solely on AI as it may produce incorrect responses.

• Failure to provide context when necessary.

• Inconsistency in prompt style: Maintain consistency in prompt formatting for model comprehension.

Ethical Considerations in Prompt Design

• Avoid prompts that lead to false or harmful information.

• Steer clear of prompts that induce biased or unfair responses.

• Always adhere to data protection laws in prompt design.

Conclusion

In conclusion, prompt engineering is an essential skill for optimizing interactions with AI models, empowering users to harness AI’s full potential while promoting responsible utilization.
I have compiled a list of recommended tools and resources for deeper exploration.
References and citations will guide you on your quest to master prompt engineering.

DeepLearning.AI
Prompt engineering
Learn Prompting

The post Enhancing AI Interaction: A Guide to Prompt Engineering appeared first on ProdSens.live.

Note-taking apps have exploded in popularity over the past decade. Giants like Evernote, OneNote, and Google Keep make it easy to digitally save personal thoughts, ideas, and memories.

However, concerns around data privacy have increased, with these apps storing troves of private information in the cloud.

This presents an opportunity for developers to build a secure, encrypted note-taking app to give users peace of mind. By using technologies like HTML, CSS, and JavaScript on the front end and encryption on the back-end, it’s possible to create an app that keeps data private while still usable and accessible.

In this article, we’ll explore the landscape of note-taking apps, outline how to build secure client-side and server-side functionality, discuss challenges around encryption, and provide best practices for a privacy-first note app. Let’s dive in!

Note-Taking App Usage and Privacy Concerns

The note-taking app market has boomed, with an estimated 600 million people using these apps in 2022. Evernote leads with over 250 million users, followed by OneNote, Google Keep, and others. Annual revenues for top note apps exceed $500 million.

Up to 68% of note app users store personally identifiable information like passwords, addresses, and credit card numbers. This has led to growing worries over data privacy. A 2022 survey found that 80% of people were concerned their sensitive data may be hacked, leaked, or exploited by note app companies.

Indeed, note apps have suffered several embarrassing breaches. In 2016 over 68 million Dropbox accounts were compromised with encrypted passwords stolen.

In 2020, Evernote had to reset user passwords after detecting unauthorized access to its network.

Note apps can leave user data vulnerable without adequate encryption and security measures.

Prerequisites

• Basic knowledge of HTML, CSS, and Javascript
• Code Editor

To get started, fork the repo from here and follow along.

Building the Note App Front-end

The first step in creating a secure note app is building an intuitive front-end interface using standard web technologies. HTML provides the content structure and semantic tags to organize the UI into logical sections:

https://gist.github.com/Scofield-Idehen/91cbba84a853922f047a282efe164b73

https://gist.github.com/Scofield-Idehen/91cbba84a853922f047a282efe164b73

The provided HTML code creates the frontend structure and layout for a secure web-based note-taking application. This allows users to store personal notes online behind a password-protected encryption system safely.

At the top level, the code contains typical HTML boilerplate like the and tags. The links to a CSS stylesheet for styling the page.

Inside the , a top navigation bar is defined with