#AI and ML in Test Automation

[woodjessica123-blog]

How is AI and ML in Test Automation Revolutionizing the Industry

AI and ML in automation have led to a 40% average increase in operational efficiency across various industries. From fitness trackers utilizing AI to enhance training, to smart home assistants simplifying daily tasks, and apps suggesting personalized recommendations for shoppers, their influence is pervasive. We’ve witnessed streaming platforms tailoring music and movie recommendations based on user data, and automation testing tools optimizing test procedures. These technologies are ubiquitous, transforming the way we interact with the world around us.

AIandMLinTestAutomation #AI #ML #TestAutomation

[techinsightweekly]

As the field of QA and software testing services continues to evolve, modern testing teams are leveraging cutting-edge technologies to enhance their QA Automation services efforts. 

[stuarttechnologybob]

What is the difference between AI testing and automation testing?

Automation Testing Services

As technology continues to evolve, so do the methods used to test software. Two popular approaches in the industry today are AI testing and Automation Testing. While they are often used together or mentioned side by side, they serve different purposes and bring unique advantages to software development. Let's explore how they differ.

What Is Automation Testing?

Automation Testing involves writing and crafting test scripts or using testing tools and resources to run the tests automatically without human intervention. As it's commonly used to speed up repetitive testing tasks like regression testing, performance checks, or functional validations. These tests follow a fixed set of rules and are often best suited for stable, predictable applications with its implementation. Automation Test improves overall efficiency, reduces human error, and helps the developers and coders to release software faster and with precise detailing.

What Is AI Testing?

AI testing uses artificial intelligence technologies like ML, NLP, and pattern recognition to boost their software testing process and operations. Unlike Automation Tests, AI testing can learn from data, predict where bugs might occur, and even adapt test cases when an application changes. While it makes the testing process more innovative and flexible, especially in complex and tough applications where manual updates to test scripts are time-consuming.

Key Differences Between AI Testing and Automation Testing:

Approach: Automation Test follows pre-written scripts, while AI testing uses the data analysis and learning to make precise decisions with ease.

Flexibility: Automation Test requires the updates if the software changes or adapts to new terms; AI testing can adapt automatically and without any interpretation.

Efficiency: While both of the testing methods aim to save time, AI testing offers more intelligent insights and better prioritization of test cases with its adaptation.

Use Cases: Automation Tests are ideal and suitable for regression tests and routine tasks and common testing. AI testing is better suited for dynamic applications and predictive testing.

Both methods are valuable, and many companies use a combination of Automation Testing and AI testing to achieve reliable and intelligent quality assurance. Choosing the correct method depends on the project's complexity and testing needs. Automation Test is best for repetitive and everyday tasks like checking login pages, payment forms, or user dashboards and analytics. It's also helpful in regression testing — where old features must be retested after  certain updates or standard system upgrades.

Companies like Suma Soft, IBM, Cyntexa, and Cignex offer advanced automation test solutions that support fast delivery, better performance, and improved software quality for businesses of all sizes.

[pixelizes]

How AI & Machine Learning Are Changing UI/UX Design

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing UI/UX design by making digital experiences more intelligent, adaptive, and user-centric. From personalized interfaces to automated design processes, AI is reshaping how designers create and enhance user experiences. In this blog, we explore the key ways AI and ML are transforming UI/UX design and what the future holds.

For more UI/UX trends and insights, visit Pixelizes Blog.

AI-Driven Personalization

One of the biggest changes AI has brought to UI/UX design is hyper-personalization. By analyzing user behavior, AI can tailor content, recommendations, and layouts to individual preferences, creating a more engaging experience.

How It Works:

AI analyzes user interactions, including clicks, time spent, and preferences.

Dynamic UI adjustments ensure users see what’s most relevant to them.

Personalized recommendations, like Netflix suggesting shows or e-commerce platforms curating product lists.

Smart Chatbots & Conversational UI

AI-powered chatbots have revolutionized customer interactions by offering real-time, intelligent responses. They enhance UX by providing 24/7 support, answering FAQs, and guiding users seamlessly through applications or websites.

Examples:

Virtual assistants like Siri, Alexa, and Google Assistant.

AI chatbots in banking, e-commerce, and healthcare.

NLP-powered bots that understand user intent and sentiment.

Predictive UX: Anticipating User Needs

Predictive UX leverages ML algorithms to anticipate user actions before they happen, streamlining interactions and reducing friction.

Real-World Applications:

Smart search suggestions (e.g., Google, Amazon, Spotify).

AI-powered auto-fill forms that reduce typing effort.

Anticipatory design like Google Maps estimating destinations.

AI-Powered UI Design Automation

AI is streamlining design workflows by automating repetitive tasks, allowing designers to focus on creativity and innovation.

Key AI-Powered Tools:

Adobe Sensei: Automates image editing, tagging, and design suggestions.

Figma AI Plugins & Sketch: Generate elements based on user input.

UX Writing Assistants that enhance microcopy with NLP.

Voice & Gesture-Based Interactions

With AI advancements, voice and gesture control are becoming standard features in UI/UX design, offering more intuitive, hands-free interactions.

Examples:

Voice commands via Google Assistant, Siri, Alexa.

Gesture-based UI on smart TVs, AR/VR devices.

Facial recognition & biometric authentication for secure logins.

AI in Accessibility & Inclusive Design

AI is making digital products more accessible to users with disabilities by enabling assistive technologies and improving UX for all.

How AI Enhances Accessibility:

Voice-to-text and text-to-speech via Google Accessibility.

Alt-text generation for visually impaired users.

Automated color contrast adjustments for better readability.

Sentiment Analysis for Improved UX

AI-powered sentiment analysis tools track user emotions through feedback, reviews, and interactions, helping designers refine UX strategies.

Uses of Sentiment Analysis:

Detecting frustration points in customer feedback.

Optimizing UI elements based on emotional responses.

Enhancing A/B testing insights with AI-driven analytics.

Future of AI in UI/UX: What’s Next?

As AI and ML continue to evolve, UI/UX design will become more intuitive, adaptive, and human-centric. Future trends include:

AI-generated UI designs with minimal manual input.

Real-time, emotion-based UX adaptations.

Brain-computer interface (BCI) integrations for immersive experiences.

Final Thoughts

AI and ML are not replacing designers—they are empowering them to deliver smarter, faster, and more engaging experiences. As we move into a future dominated by intelligent interfaces, UI/UX designers must embrace AI-powered design methodologies to create more personalized, accessible, and user-friendly digital products.

Explore more at Pixelizes.com for cutting-edge design insights, AI tools, and UX trends.

[apexbyte]

What is artificial intelligence (AI)?

Imagine asking Siri about the weather, receiving a personalized Netflix recommendation, or unlocking your phone with facial recognition. These everyday conveniences are powered by Artificial Intelligence (AI), a transformative technology reshaping our world. This post delves into AI, exploring its definition, history, mechanisms, applications, ethical dilemmas, and future potential.

What is Artificial Intelligence? Definition: AI refers to machines or software designed to mimic human intelligence, performing tasks like learning, problem-solving, and decision-making. Unlike basic automation, AI adapts and improves through experience.

Brief History:

1950: Alan Turing proposes the Turing Test, questioning if machines can think.

1956: The Dartmouth Conference coins the term "Artificial Intelligence," sparking early optimism.

1970s–80s: "AI winters" due to unmet expectations, followed by resurgence in the 2000s with advances in computing and data availability.

21st Century: Breakthroughs in machine learning and neural networks drive AI into mainstream use.

How Does AI Work? AI systems process vast data to identify patterns and make decisions. Key components include:

Machine Learning (ML): A subset where algorithms learn from data.

Supervised Learning: Uses labeled data (e.g., spam detection).

Unsupervised Learning: Finds patterns in unlabeled data (e.g., customer segmentation).

Reinforcement Learning: Learns via trial and error (e.g., AlphaGo).

Neural Networks & Deep Learning: Inspired by the human brain, these layered algorithms excel in tasks like image recognition.

Big Data & GPUs: Massive datasets and powerful processors enable training complex models.

Types of AI

Narrow AI: Specialized in one task (e.g., Alexa, chess engines).

General AI: Hypothetical, human-like adaptability (not yet realized).

Superintelligence: A speculative future AI surpassing human intellect.

Other Classifications:

Reactive Machines: Respond to inputs without memory (e.g., IBM’s Deep Blue).

Limited Memory: Uses past data (e.g., self-driving cars).

Theory of Mind: Understands emotions (in research).

Self-Aware: Conscious AI (purely theoretical).

Applications of AI

Healthcare: Diagnosing diseases via imaging, accelerating drug discovery.

Finance: Detecting fraud, algorithmic trading, and robo-advisors.

Retail: Personalized recommendations, inventory management.

Manufacturing: Predictive maintenance using IoT sensors.

Entertainment: AI-generated music, art, and deepfake technology.

Autonomous Systems: Self-driving cars (Tesla, Waymo), delivery drones.

Ethical Considerations

Bias & Fairness: Biased training data can lead to discriminatory outcomes (e.g., facial recognition errors in darker skin tones).

Privacy: Concerns over data collection by smart devices and surveillance systems.

Job Displacement: Automation risks certain roles but may create new industries.

Accountability: Determining liability for AI errors (e.g., autonomous vehicle accidents).

The Future of AI

Integration: Smarter personal assistants, seamless human-AI collaboration.

Advancements: Improved natural language processing (e.g., ChatGPT), climate change solutions (optimizing energy grids).

Regulation: Growing need for ethical guidelines and governance frameworks.

Conclusion AI holds immense potential to revolutionize industries, enhance efficiency, and solve global challenges. However, balancing innovation with ethical stewardship is crucial. By fostering responsible development, society can harness AI’s benefits while mitigating risks.

[raomarketingpro]

Free AI Tools

Artificial Intelligence (AI) has revolutionized the way we work, learn, and create. With an ever-growing number of tools, it’s now easier than ever to integrate AI into your personal and professional life without spending a dime. Below, we’ll explore some of the best free AI tools across various categories, helping you boost productivity, enhance creativity, and automate mundane tasks.

Wanna know about free ai tools

1. Content Creation Tools

ChatGPT (OpenAI)

One of the most popular AI chatbots, ChatGPT, offers a free plan that allows users to generate ideas, write content, answer questions, and more. Its user-friendly interface makes it accessible for beginners and professionals alike.

Best For:

Writing articles, emails, and brainstorming ideas.

Limitations:

Free tier usage is capped; may require upgrading for heavy use.

Copy.ai

Copy.ai focuses on helping users craft engaging marketing copy, blog posts, and social media captions.

2. Image Generation Tools

DALL·EOpenAI’s DALL·E can generate stunning, AI-created artwork from text prompts. The free tier allows users to explore creative possibilities, from surreal art to photo-realistic images.

Craiyon (formerly DALL·E Mini)This free AI image generator is great for creating quick, fun illustrations. It’s entirely free but may not match the quality of professional tools.

3. Video Editing and Creation

Runway MLRunway ML offers free tools for video editing, including AI-based background removal, video enhancement, and even text-to-video capabilities.

Pictory.aiTurn scripts or blog posts into short, engaging videos with this free AI-powered tool. Pictory automates video creation, saving time for marketers and educators.

4. Productivity Tools

Notion AINotion's AI integration enhances the already powerful productivity app. It can help generate meeting notes, summarize documents, or draft content directly within your workspace.

Otter.aiOtter.ai is a fantastic tool for transcribing meetings, interviews, or lectures. It offers a free plan that covers up to 300 minutes of transcription monthly.

5. Coding and Data Analysis

GitHub Copilot (Free for Students)GitHub Copilot, powered by OpenAI, assists developers by suggesting code and speeding up development workflows. It’s free for students with GitHub’s education pack.

Google ColabGoogle’s free cloud-based platform for coding supports Python and is perfect for data science projects and machine learning experimentation.

6. Design and Presentation

Canva AICanva’s free tier includes AI-powered tools like Magic Resize and text-to-image generation, making it a top choice for creating professional presentations and graphics.

Beautiful.aiThis AI presentation tool helps users create visually appealing slides effortlessly, ideal for professionals preparing pitch decks or educational slides.

7. AI for Learning

Duolingo AIDuolingo now integrates AI to provide personalized feedback and adaptive lessons for language learners.

Khanmigo (from Khan Academy)This AI-powered tutor helps students with math problems and concepts in an interactive way. While still in limited rollout, it’s free for Khan Academy users.

Why Use Free AI Tools?

Free AI tools are perfect for testing the waters without financial commitments. They’re particularly valuable for:

Conclusion

AI tools are democratizing access to technology, allowing anyone to leverage advanced capabilities at no cost. Whether you’re a writer, designer, developer, or educator, there’s a free AI tool out there for you. Start experimenting today and unlock new possibilities!

4o

[xaltius]

The Dos and Don’ts of AI & ML in Digital Marketing

Artificial intelligence (AI) and machine learning (ML) are revolutionizing the digital marketing landscape, offering unprecedented opportunities for personalization, automation, and optimization. However, like any powerful tool, AI and ML must be wielded wisely. This blog outlines the dos and don'ts of leveraging these technologies effectively in your digital marketing strategies.

The Dos:

Do Define Clear Objectives: Before implementing any AI/ML solution, clearly define your marketing goals. What are you trying to achieve? Increased conversions? Improved customer engagement? Specific objectives will guide your AI/ML strategy and ensure you're measuring the right metrics.

Do Focus on Data Quality: AI/ML algorithms are only as good as the data they are trained on. Prioritize collecting clean, accurate, and relevant data. Invest in data cleansing and validation processes to ensure the reliability of your AI-driven insights.

Do Start Small and Iterate: Don't try to implement everything at once. Begin with a specific use case, such as automating social media posting or personalizing email campaigns. Test, refine, and iterate on your approach before scaling up.

Do Prioritize Personalization: AI/ML excels at personalization. Leverage these technologies to create tailored content, product recommendations, and offers for individual customers based on their behavior, preferences, and demographics.

Do Embrace Automation: AI can automate repetitive tasks, freeing up marketers to focus on strategic initiatives. Identify areas where AI can streamline workflows, such as ad campaign optimization, content curation, or customer service interactions.

Do Focus on Transparency and Explainability: Understand how your AI/ML models work and ensure they are transparent and explainable. This is crucial for building trust and addressing ethical concerns.

Do Measure and Analyze Results: Track the performance of your AI/ML-driven marketing campaigns and analyze the data to identify areas for improvement. Use data to inform your decisions and optimize your strategies.

Do Stay Updated: The field of AI/ML is constantly evolving. Keep up with the latest advancements, new tools, and best practices to ensure you're maximizing the potential of these technologies.

The Don'ts:

Don't Treat AI as a Magic Bullet: AI/ML is a powerful tool, but it's not a magic solution. It requires careful planning, implementation, and ongoing management. Don't expect overnight results without putting in the effort.

Don't Neglect Human Oversight: While AI can automate tasks, it's essential to maintain human oversight. Human judgment is still crucial for strategic decision-making, creative development, and ethical considerations.

Don't Over-Rely on Automation: While automation is beneficial, don't over-automate to the point where you lose the human touch. Maintain a balance between automation and human interaction to ensure a personalized and engaging customer experience.

Don't Ignore Ethical Implications: AI/ML raises ethical concerns about data privacy, bias, and transparency. Be mindful of these issues and ensure that your AI-driven marketing practices are ethical and responsible.

Don't Forget About Data Security: Protecting customer data is paramount. Implement robust security measures to safeguard your data from unauthorized access and breaches.

Don't Be Afraid to Experiment: AI/ML is a field of experimentation. Don't be afraid to try new approaches, test different algorithms, and learn from your mistakes.

Don't Underestimate the Importance of Training: Proper training is essential for effectively using AI/ML tools and understanding their capabilities and limitations. Invest in training for your marketing team to ensure they have the skills they need to succeed.

Digital Marketing & AI Certification Program: Your Path to AI-Powered Marketing Mastery

Want to become a sought-after digital marketing professional with expertise in AI and ML? Consider enrolling in a Digital Marketing & AI Certification Program. These programs provide comprehensive training on the latest AI/ML tools and techniques, preparing you to leverage the power of these technologies in your marketing strategies. You'll learn how to:

Develop and implement AI/ML-driven marketing campaigns.

Analyze data and generate actionable insights.

Choose and use the right AI/ML marketing tools.

Address ethical considerations related to AI/ML in marketing.

Conclusion:

AI and ML are transforming the landscape of digital marketing, offering unprecedented opportunities for growth and innovation. By following these dos and don'ts, marketers can harness the power of these technologies effectively and responsibly, driving better results and achieving their marketing goals. The future of digital marketing is intelligent, and it's powered by AI and ML.

[christianbale121]

AI Agent Development: How to Create Intelligent Virtual Assistants for Business Success

In today's digital landscape, businesses are increasingly turning to AI-powered virtual assistants to streamline operations, enhance customer service, and boost productivity. AI agent development is at the forefront of this transformation, enabling companies to create intelligent, responsive, and highly efficient virtual assistants. In this blog, we will explore how to develop AI agents and leverage them for business success.

Understanding AI Agents and Virtual Assistants

AI agents, or intelligent virtual assistants, are software programs that use artificial intelligence, machine learning, and natural language processing (NLP) to interact with users, automate tasks, and make decisions. These agents can be deployed across various platforms, including websites, mobile apps, and messaging applications, to improve customer engagement and operational efficiency.

Key Features of AI Agents

Natural Language Processing (NLP): Enables the assistant to understand and process human language.

Machine Learning (ML): Allows the assistant to improve over time based on user interactions.

Conversational AI: Facilitates human-like interactions.

Task Automation: Handles repetitive tasks like answering FAQs, scheduling appointments, and processing orders.

Integration Capabilities: Connects with CRM, ERP, and other business tools for seamless operations.

Steps to Develop an AI Virtual Assistant

1. Define Business Objectives

Before developing an AI agent, it is crucial to identify the business goals it will serve. Whether it's improving customer support, automating sales inquiries, or handling HR tasks, a well-defined purpose ensures the assistant aligns with organizational needs.

2. Choose the Right AI Technologies

Selecting the right technology stack is essential for building a powerful AI agent. Key technologies include:

NLP frameworks: OpenAI's GPT, Google's Dialogflow, or Rasa.

Machine Learning Platforms: TensorFlow, PyTorch, or Scikit-learn.

Speech Recognition: Amazon Lex, IBM Watson, or Microsoft Azure Speech.

Cloud Services: AWS, Google Cloud, or Microsoft Azure.

3. Design the Conversation Flow

A well-structured conversation flow is crucial for user experience. Define intents (what the user wants) and responses to ensure the AI assistant provides accurate and helpful information. Tools like chatbot builders or decision trees help streamline this process.

4. Train the AI Model

Training an AI assistant involves feeding it with relevant datasets to improve accuracy. This may include:

Supervised Learning: Using labeled datasets for training.

Reinforcement Learning: Allowing the assistant to learn from interactions.

Continuous Learning: Updating models based on user feedback and new data.

5. Test and Optimize

Before deployment, rigorous testing is essential to refine the AI assistant's performance. Conduct:

User Testing: To evaluate usability and responsiveness.

A/B Testing: To compare different versions for effectiveness.

Performance Analysis: To measure speed, accuracy, and reliability.

6. Deploy and Monitor

Once the AI assistant is live, continuous monitoring and optimization are necessary to enhance user experience. Use analytics to track interactions, identify issues, and implement improvements over time.

Benefits of AI Virtual Assistants for Businesses

1. Enhanced Customer Service

AI-powered virtual assistants provide 24/7 support, instantly responding to customer queries and reducing response times.

2. Increased Efficiency

By automating repetitive tasks, businesses can save time and resources, allowing employees to focus on higher-value tasks.

3. Cost Savings

AI assistants reduce the need for large customer support teams, leading to significant cost reductions.

4. Scalability

Unlike human agents, AI assistants can handle multiple conversations simultaneously, making them highly scalable solutions.

5. Data-Driven Insights

AI assistants gather valuable data on customer behavior and preferences, enabling businesses to make informed decisions.

Future Trends in AI Agent Development

1. Hyper-Personalization

AI assistants will leverage deep learning to offer more personalized interactions based on user history and preferences.

2. Voice and Multimodal AI

The integration of voice recognition and visual processing will make AI assistants more interactive and intuitive.

3. Emotional AI

Advancements in AI will enable virtual assistants to detect and respond to human emotions for more empathetic interactions.

4. Autonomous AI Agents

Future AI agents will not only respond to queries but also proactively assist users by predicting their needs and taking independent actions.

Conclusion

AI agent development is transforming the way businesses interact with customers and streamline operations. By leveraging cutting-edge AI technologies, companies can create intelligent virtual assistants that enhance efficiency, reduce costs, and drive business success. As AI continues to evolve, embracing AI-powered assistants will be essential for staying competitive in the digital era.

[aionlinemoney]

The Role of Machine Learning Engineer: Combining Technology and Artificial Intelligence

Artificial intelligence has transformed our daily lives in a greater way than we can’t imagine over the past year, Impacting how we work, communicate, and solve problems. Today, Artificial intelligence furiously drives the world in all sectors from daily life to the healthcare industry. In this blog we will learn how machine learning engineer build systems that learn from data and get better over time, playing a huge part in the development of artificial intelligence (AI). Artificial intelligence is an important field, making it more innovative in every industry. In the blog, we will look career in Machine learning in the field of engineering.

What is Machine Learning Engineering?

Machine Learning engineer is a specialist who designs and builds AI models to make complex challenges easy. The role in this field merges data science and software engineering making both fields important in this field. The main role of a Machine learning engineer is to build and design software that can automate AI models. The demand for this field has grown in recent years. As Artificial intelligence is a driving force in our daily needs, it become important to run the AI in a clear and automated way.

A machine learning engineer creates systems that help computers to learn and make decisions, similar to human tasks like recognizing voices, identifying images, or predicting results. Not similar to regular programming, which follows strict rules, machine learning focuses on teaching computers to find patterns in data and improve their predictions over time.

Responsibility of a Machine Learning Engineer:

Collecting and Preparing Data

Machine learning needs a lot of data to work well. These engineers spend a lot of time finding and organizing data. That means looking for useful data sources and fixing any missing information. Good data preparation is essential because it sets the foundation for building successful models.

Building and Training Models

The main task of Machine learning engineer is creating models that learn from data. Using tools like TensorFlow, PyTorch, and many more, they build proper algorithms for specific tasks. Training a model is challenging and requires careful adjustments and monitoring to ensure it’s accurate and useful.

Checking Model Performance

When a model is trained, then it is important to check how well it works. Machine learning engineers use scores like accuracy to see model performance. They usually test the model with separate data to see how it performs in real-world situations and make improvements as needed.

Arranging and Maintaining the Model

After testing, ML engineers put the model into action so it can work with real-time data. They monitor the model to make sure it stays accurate over time, as data can change and affect results. Regular updates help keep the model effective.

Working with Other Teams

ML engineers often work closely with data scientists, software engineers, and experts in the field. This teamwork ensures that the machine learning solution fits the business goals and integrates smoothly with other systems.

Important skill that should have to become Machine Learning Engineer:

Programming Languages

Python and R are popular options in machine learning, also other languages like Java or C++ can also help, especially for projects needing high performance.

Data Handling and Processing

Working with large datasets is necessary in Machine Learning. ML engineers should know how to use SQL and other database tools and be skilled in preparing and cleaning data before using it in models.

Machine Learning Structure

ML engineers need to know structure like TensorFlow, Keras, PyTorch, and sci-kit-learn. Each of these tools has unique strengths for building and training models, so choosing the right one depends on the project.

Mathematics and Statistics

A strong background in math, including calculus, linear algebra, probability, and statistics, helps ML engineers understand how algorithms work and make accurate predictions.

Why to become a Machine Learning engineer?

A career as a machine learning engineer is both challenging and creative, allowing you to work with the latest technology. This field is always changing, with new tools and ideas coming up every year. If you like to enjoy solving complex problems and want to make a real impact, ML engineering offers an exciting path.

Conclusion 

Machine learning engineer plays an important role in AI and data science, turning data into useful insights and creating systems that learn on their own. This career is great for people who love technology, enjoy learning, and want to make a difference in their lives. With many opportunities and uses, Artificial intelligence is a growing field that promises exciting innovations that will shape our future. Artificial Intelligence is changing the world and we should also keep updated our knowledge in this field, Read AI related latest blogs here.

[fishboneart]

Fishbone #010

the human cost rolls downhill

5 source images, 9 layers.

I hate having to write this. I hate that this is a thing that happened to be written about.

In early 2024 a private virtual clinic providing medical care for a vulnerable and underserved patient demographic allegedly replaced 80% of its human staff with machine learning software.

As far as I can find this hasn't been reported on in the media so far and many of the details are currently not public record. I can't confirm how many staff were laid off, how many quit, how many remain, and how many of those are medics vs how many are admin. I can't confirm exact dates or software applications. This uncertainty about key details is why I'm not naming the clinic. I don't want to accidentally do a libel.

I'm not a journalist and ancestors willing researching this post is as close as I'll ever have to get. It's been extremely depressing. The patient testimonials are abundant and harrowing.

What I have been able to confirm is that the clinic has publicly announced they are "embracing AI," and their FAQs state that their "algorithms" assess patients' medical history, create personalised treatment plans, and make recommendations for therapies, tests, and medications. This made me scream out loud in horror.

Exploring the clinic's family of sites I found that they're using Zoho to manage appointment scheduling. I don't know what if any other applications they're using Zoho for, or whether they're using other software alongside it. Zoho provides office, collaboration, and customer relationship management products; things like scheduling, videocalls, document sharing, mail sorting, etc.

The clinic's recent Glassdoor reviews are appalling, and make reference to increased automation, layoffs, and hasty ai implementation.

The patient community have been reporting abnormally high rates of inadequate and inappropriate care since late February/early March, including:

Wrong or incomplete prescriptions

Inability to contact the clinic

Inability to cancel recurring payments

Appointments being cancelled

Staff simply failing to attend appointments

Delayed prescriptions

Wrong or incomplete treatment summaries

Unannounced dosage or medication changes

The clinic's FAQ suggests that this is a temporary disruption while the new automation workflows are implemented, and service should stabilise in a few months as the new workflows come online. Frankly I consider this an unacceptable attitude towards human lives and health. Existing stable workflows should not be abandoned until new ones are fully operational and stable. Ensuring consistent and appropriate care should be the highest priority at all times.

The push to introduce general-use machine learning into specialised areas of medicine is a deadly one. There are a small number of experimental machine learning models that may eventually have limited use in highly specific medical contexts, to my knowledge none are currently commercially available. No commercially available current generation general use machine learning model is suitable or safe for medical use, and it's almost certain none ever will be.

Machine learning simply doesn't have the capacity to parse the nuances of individual health needs. It doesn't have the capacity to understand anything, let alone the complexities of medical care. It amplifies bias and it "hallucinates" and current research indicates there's no way to avoid either. All it will take for patients to die is for a ML model to hallucinate an improper diagnosis or treatment that's rubber stamped by an overworked doctor.

Yet despite the fact that it is not and will never be fit for purpose, general use machine learning has been pushed fait accompli into the medical lives of real patients, in service to profit. Whether the clinic itself or the software developers or both, someone is profiting from this while already underserved and vulnerable patients are further neglected and endangered.

This is inevitable by design. Maximising profit necessitates inserting the product into as many use cases as possible irrespective of appropriateness. If not this underserved patient group, another underserved patient group would have been pressed, unconsenting, into unsupervised experiments in ML medicine--and may still. The fewer options and resources people have, the easier they are to coerce. You can do whatever you want to those who have no alternative but to endure it.

For profit to flow upwards, cost must flow downwards. This isn't an abstract numerical principle it's a deadly material fact. Human beings, not abstractions, bear the cost of the AI bubble. The more marginalised and exploited the human beings, the more of the cost they bear. Overexploited nations bear the burden of mining, manufacture, and pollution for the physical infrastructure to exist, overexploited workers bear the burden of making machine learning function at all (all of which I will write more about another day), and now patients who don't have the option to refuse it bear the burden of its overuse. There have been others. There will be more. If the profit isn't flowing to you, the cost is--or it will soon.

It doesn't have to be like this. It's like this because humans made it this way, we could change it. Indeed, we must if we are to survive.

[glitchypsi]

ENDTHOUGHTS: CA-6 & POKERMAN23

Pokerman is the codename for the underlying card-game engine (not Unity's parts) of the GLITCHYPSI CA-6 game, which was made for Artfight as a means to challenge myself in a gamejam-esque fashion. I was kind of soured by the fact it didn't garner attention by the Artfight staff despite the effort I put into it, but even in light of that, I have employed techniques that I had not used before which have increased my confidence as a game developer, and I kind of enjoyed it even if I pulled an allnighter to complete this one in time, with my job and all...

Pokerman started as an exclusively CLI application, as my approach this time was to separate the actual card game logic from the game itself. Kind of an engine-frontend separation, which would allow the engine to be extensible and also ported to many other "frontends" if so I wished, so the game could exist in Unity, maybe even MonoGame, as a webapp with a C# backed server, and so on.

I worked on the "dealer" and card values first. The way the engine is set up is that every hand is its own instance that checks a hand's cards, and if it matches, it puts itself as the "Hand result" for that hand, and that is used to determine the weight of the hand, which can be compared to other hands to decide which is stronger.

This approach makes it easy for me to add new hands, for instance. Currently, the only existing Hand Results are all the results that pertain to the 5-card hold rules with no jokers.

What the engine does is use a set of hand instances and check from strongest to weakest until the first one yields true. The last one will always happen, as it is the bogus hand. The only chain is, as expected, the 5-card hold one.

This, the hand weight comparisons, and the tie mechanics were perhaps the places I debugged in the most to check for potential pitfalls. With the console and specific non-automated tests, it was kind of easy and fun, although... the code... might use some work.

Next I worked on the AI. With all the ML fuzz I must state that this AI is merely a random chance AI. No machine learning is involved in this AI.

All this AI does is look at a chance table given a number of cards to replace which determines the chances of getting the wanted hand with X cards to replace. The table is precomputed. (1 = 100%)

Some of the values may be probabilistcally wrong. My combinatorics aren't as polished as they used to be, and instead of using raw math I took a simpler approach and used the engine itself to generate 10k pseudorandom (System.Random() .NET 6 implementation) draws to determine a percentage. However, for the purposes of the game engine, and given extreme precision was not necessary, it's acceptable.

Risk and Contentness were a pair of values that determined whether the AI would risk discarding their current hand for a stronger one. Contentness was tested for first. If it met the margin, it would draw no new cards. Riskiness was tested for last. If it met the margin, it would go up one hand in terms of hand weight to aim for. It then gave back the result evaluation that it desired to get the cards for, or, if content, the actual hand (signifying contentness with the current hand).

It would've made a fair game with a degree of difficulty that wasn't ever the same, and the AI risk and contentness parameters can be changed at any moment during runtime.

This was all for the underlying engine. Next was to actually link all these faceless bits of data to something the player could interact with...

...and while at work, when I had completed all my assigned development projects (under supervision of my boss, so I was not just slacking!!!) I made a placeholder set of cards.

These were used to make a Unity ScriptableObject. This was also my first time working with ScriptableObjects themselves, but a dear friend had shown me their power before and I was raring to try them out for this project.

The data is reminiscent of the Pokerman Engine Card (structure below), with added relevant Unity information.

The approach of using ScriptableObjects with their own methods I could call to, for example, get the sprite for a particular Card, was extremely useful, because I could write code once and make seven thousand themed decks if I so wanted.

I used this to make the world-space and GUI space cards that you can touch in the game.

Yes. they DO flip, literally. It was the easier way to do this, and in the context of the game (and shader used) you couldn't just cheat out of that.

After the cards were set in place, it was time to make the dealer..................................... oh mama

The assets often are the hardest part of these things. They take so long because animations are something else. The first dealer was

hm

this.

Yes, that is positively a Henohenomoheji.

It was simple, sketchy and scratchy, which was perfect to test out animations with. Lord, it did take some time still. Animating is hard. Praise animators.

I had some fun playing with it, not gonna lie.

After all this, it was time for the real deal.

Oh boy. Remember when I said animation was hard? With such tight timeline to work with and having to work in my usual artstyle?

Yeah. Look at this.

This took me a relatively mean amount of hours (entire afternoon), and it was the final day I could work on this game by the time I had started making this spritesheet.

It is composed of individual parts to make my life easier and then put together in Aseprite. It was probably the part that took me the most time out of everything else asset related. It even still has some flaws, such as the body sometimes shifting positions between frames. My bad.

After that I worked on the cards. They were abstract-ish representations of the characters I had chosen to become cards as part of my Arfight gimmick for this year, and a Casandra themed cardback.

Fun fact. Card with value 1 was gonna be somebody different.

After everything was put together......... it was time for the audio. I left audio for last. I suck at music. I have said it once I will keep saying it and it does NOT seem to be getting any better, and it was not going to improve within the remaining 12 hours I had to work on the game. I could not afford time to laze off in FL Studio when I had to polish some stuff to make the game get out there. I wasn't confident in my music skills at all (in contrast to my programming and graphics skills).

I got some sfx from freesound and edited it to match, and used some of the stream assets for other sound effects related to the coins (taken straight from Mario Party 3, ha ha)

The music track... I had asked another dear friend about it when development was still in the console-app stage, but they said they wouldn't be available for such. So, I just left it for the very last moment. I was then SCRAMBLING to find a nice track to use that would not get me in trouble (I did not use Luigi's Casino's track for this reason), so I looked where I get some of my tracks I play while programming, and settled on this one...

I liked it for the ambience (background) I pulled at the last minute (because I HADN'T even drawn the background then yet!!!) and with that, it just needed to be polished and uploaded to itch and Artfight (most importantly Artfight!!!!)

I pulled an allnighter finishing the final details of the game and authoring the page for it in itch. I had done allnighters before for other games I had made for people I cherished (although those are... probably better left in the past now.)

And so while at work I published the game and posted it on Artfight.

It sure was a ride and yes I will mention again I am sort of mad I was not even mentioned among the games that were highlighted in the final Artfight blogpost despite all the effort I put into it. However... the people the game featured really liked it, and that warmed my heart and justified my effort, as well as what I learned while making it.

This game was a promise to myself to complete, since in 2021 my computer was stolen while I was making a Subway Surfers clone for the same reasoning. I wanted to get it done by all means necessary, and I can now reliably say I have completed my goal.

Hope you enjoy the game. I am going to polish it a little bit more and upload it to Newgrounds at a later date... but no more allnighters over it.

It's done. It's cute, it's small, and it's something I should've done when I first started my gamedev career instead of trying to lift the big pumpkins first without any muscle.

Maybe I'll make Pokerman open source... or make another game using it, extending it to be able to properly handle Hold'Em Poker.

Want to play the game? Click "CA-6" in the title of this post.

[techinsightweekly]

We emphasize our QA automation and software testing expertise for root cause analysis in collaboration with IT specialists responsible for specific systems like CRM, ERP, and CMS, aiming to tackle data quality issues at their source.

[thara333]

Pioneering the Future of Software Quality Assurance through Automation Testing

Automation testing, a dynamic and essential software quality assurance technique, is reshaping the landscape of application testing. Its mission is to execute predefined test cases on applications, delivering heightened accuracy, reliability, and efficiency by automating tedious and time-consuming testing tasks.

The Present and Future Synergy in Automation Testing:

At its essence, automation testing responds to the pressing need for a streamlined testing process. Beyond its current significance, the future promises a paradigm shift, characterized by transformative trends and advancements.

Unveiling Future Trends in Automation Testing:

Proactive "Shift-Left" Testing: Embracing a proactive "Shift-Left" approach, the future of automation testing integrates testing earlier in the development life cycle. This strategic shift aims to detect and address issues at their inception, fostering a more resilient and efficient software development process.

Harmonizing with DevOps: Automation is positioned to become increasingly integral to DevOps practices. Its seamless integration into continuous integration and delivery (CI/CD) pipelines ensures not just faster but more reliable releases, aligning seamlessly with the agile principles of DevOps.

AI and Machine Learning Synergy: The convergence of artificial intelligence (AI) and machine learning (ML) is poised to revolutionize automation testing. This integration enhances script maintenance, facilitates intelligent test case generation, and empowers predictive analysis based on historical data, ushering in a new era of adaptive and efficient testing processes.

Evolving Cross-Browser and Cross-Platform Testing: In response to the diversification of the software landscape, automation tools are evolving to provide robust solutions for cross-browser and cross-platform testing. Ensuring compatibility across diverse environments becomes paramount for delivering a seamless user experience.

Codeless Automation Revolution: The ascent of codeless automation tools represents a pivotal shift in testing methodologies. This trend simplifies testing processes, enabling testers with limited programming skills to create and execute automated tests. This democratization of testing accelerates adoption across teams, fostering a collaborative testing environment.

Concluding the Journey: Navigating Future Imperatives:

In conclusion, automation testing transcends its current role as a necessity, emerging as a future imperative in the ever-evolving landscape of software development. As technologies advance and methodologies mature, automation testing is poised to play a pivotal role in ensuring the delivery of high-quality software at an accelerated pace. Embracing these future trends, the software industry is set to embark on a transformative journey towards more efficient, adaptive, and reliable testing processes.

[mercyjuliet1512]

The Future of Selenium with Python: Navigating the Landscape of Web Automation

Selenium with Python is a formidable force in the world of web automation and testing. The seamless combination of the powerful Selenium framework and the simplicity and versatility of the Python programming language has made it a go-to choice for professionals in the software testing and quality assurance domain. But what does the future hold for Selenium with Python? In this blog, we'll take a deep dive into the trajectory of this dynamic duo. We'll explore its growing adoption, integration with cutting-edge technologies, support for modern web developments, advancements in performance testing, and its expanding role in mobile testing. Additionally, we'll shed light on the significance of structured training, with a special mention of ACTE Technologies and their Selenium with Python training programs, which are designed to keep professionals at the forefront of this ever-evolving landscape.

The Future of Selenium with Python:

1. Increased Adoption:

Selenium with Python has garnered significant attention and adoption in the software testing community. Its streamlined approach to web automation and testing, coupled with the popularity of Python, has contributed to its rise. In the future, we can expect this trend to continue as more organizations realize the efficiency and reliability of this combination. As businesses seek faster development cycles and higher quality software, Selenium with Python will remain a strong contender due to its effectiveness and ease of use.

2. Integration with AI and Machine Learning:

One of the most exciting aspects of Selenium with Python's future is its integration with artificial intelligence (AI) and machine learning (ML). AI and ML are transforming the landscape of software testing. These technologies enable test automation to become smarter and more adaptive. Selenium with Python can harness the power of AI and ML for various aspects of testing, including:

Smarter Test Case Design: AI algorithms can analyze application behavior and automatically design test cases that cover critical functionalities.

Self-Healing Tests: AI can identify and address issues in test scripts, making them resilient to changes in the application.

Improved Test Reporting: Machine learning can provide deeper insights into test results, identifying patterns and anomalies that human testers might miss.

3. Support for Modern Web Technologies:

The web is a constantly evolving landscape with new technologies and frameworks emerging regularly. To remain relevant, Selenium with Python continues to adapt and enhance its support for these modern web technologies. This adaptability ensures that testers can confidently navigate the complexities of web development, even with the most cutting-edge frameworks and technologies. Whether it's single-page applications, responsive web designs, or complex web components, Selenium with Python is likely to stay ahead of the curve.

4. Enhanced Performance Testing:

Performance testing is critical for ensuring that web applications can handle expected loads and provide a seamless user experience. Selenium with Python is poised to further improve its capabilities in this area. As performance testing becomes an integral part of the testing process, this combination will help organizations identify and resolve performance bottlenecks efficiently. With enhanced load testing capabilities, Selenium with Python can simulate real-world traffic and stress test applications, allowing businesses to proactively address performance issues.

5. Mobile Testing:

With the exponential growth of mobile usage, mobile testing has gained paramount importance. Selenium with Python is expected to continue expanding its capabilities in mobile testing. This means it will not only serve as a robust solution for web automation but also as a versatile choice for mobile application testing. As the lines between web and mobile applications blur, the ability to seamlessly automate and test across both platforms becomes a valuable asset.

6. The Role of Training:

In an environment where technology evolves at a rapid pace, structured training is indispensable. Training ensures that professionals are well-prepared to tackle the challenges of an ever-changing landscape. ACTE Technologies, a distinguished training institution, recognizes the importance of equipping individuals with the right skills and knowledge. Their Selenium with Python training programs are designed to provide hands-on experience, real-world examples, and a comprehensive understanding of the intricacies of web automation and testing.

The future of Selenium with Python is bright, marked by increased adoption, integration with AI and ML, support for modern web technologies, enhanced performance testing, and an expanding role in mobile testing. To excel in this evolving domain, structured training is crucial. ACTE Technologies' Selenium with Python training programs prepare professionals to stay competitive in the dynamic world of software testing and automation. With the right skills and knowledge, you can embrace the exciting journey ahead.

Selenium with Python is well-poised for a bright future. Its growing adoption, integration with AI and ML, support for modern web technologies, enhanced performance testing, and expanding role in mobile testing make it a robust choice for web automation. Training from institutions like ACTE Technologies ensures that professionals are equipped to thrive in this ever-evolving landscape.

[gradsireninc]

How To Get An Online Internship In the IT Sector (Skills And Tips)

Internships provide invaluable opportunities to gain practical skills, build professional networks, and get your foot in the door with top tech companies.

With remote tech internships exploding in IT, online internships are now more accessible than ever. Whether a college student or career changer seeking hands-on IT experience, virtual internships allow you to work from anywhere.

However, competition can be fierce, and simply applying is often insufficient. Follow this comprehensive guide to develop the right technical abilities.

After reading this, you can effectively showcase your potential, and maximize your chances of securing a remote tech internship.

Understand In-Demand IT Skills

The first step is gaining a solid grasp of the most in-demand technical and soft skills. While specific requirements vary by company and role, these competencies form a strong foundation:

Technical Skills:

Proficiency in programming languages like Python, JavaScript, Java, and C++

Experience with front-end frameworks like React, Angular, and Vue.js

Back-end development skills - APIs, microservices, SQL databases Cloud platforms such as AWS, Azure, Google Cloud

IT infrastructure skills - servers, networks, security

Data science abilities like SQL, R, Python

Web development and design

Mobile app development - Android, iOS, hybrid

Soft Skills:

Communication and collaboration

Analytical thinking and problem-solving

Leadership and teamwork

Creativity and innovation

Fast learning ability

Detail and deadline-oriented

Flexibility and adaptability

Obtain Relevant Credentials

While hands-on skills hold more weight, relevant academic credentials and professional IT certifications can strengthen your profile. Consider pursuing:

Bachelor’s degree in Computer Science, IT, or related engineering fields

Internship-specific courses teaching technical and soft skills

Certificates like CompTIA, AWS, Cisco, Microsoft, Google, etc.

Accredited boot camp programs focusing on applied skills

MOOCs to build expertise in trending technologies like AI/ML, cybersecurity

Open source contributions on GitHub to demonstrate coding skills

The right credentials display a work ethic and supplement practical abilities gained through projects.

Build An Impressive Project Portfolio

Nothing showcases skills better than real-world examples of your work. Develop a portfolio of strong coding, design, and analytical projects related to your target internship field.

Mobile apps - publish on app stores or use GitHub project pages

Websites - deploy online via hosting services

Data science - showcase Jupyter notebooks, visualizations

Open source code - contribute to public projects on GitHub

Technical writing - blog posts explaining key concepts

Automation and scripts - record demo videos

Choose projects demonstrating both breadth and depth. Align them to skills required for your desired internship roles.

Master Technical Interview Skills

IT internship interviews often include challenging technical questions and assessments. Be prepared to:

Explain your code and projects clearly. Review them beforehand.

Discuss concepts related to key technologies on your resume. Ramp up on fundamentals.

Solve coding challenges focused on algorithms, data structures, etc. Practice online judges like LeetCode.

Address system design and analytical problems. Read case interview guides.

Show communication and collaboration skills through pair programming tests.

Ask smart, well-researched questions about the company’s tech stack, projects, etc.

Schedule dedicated time for technical interview practice daily. Learn to think aloud while coding and get feedback from peers.

Show Passion and Curiosity

Beyond raw skills, demonstrating genuine passion and curiosity for technology goes a long way.

Take online courses and certifications beyond the college curriculum

Build side projects and engage in hackathons for self-learning

Stay updated on industry news, trends, and innovations

Be active on forums like StackOverflow to exchange knowledge

Attend tech events and conferences

Participate in groups like coding clubs and prior internship programs

Follow tech leaders on social mediaListen to tech podcasts while commuting

Show interest in the company’s mission, products, and culture

This passion shines through in interviews and applications, distinguishing you from other candidates.

Promote Your Personal Brand

In the digital age, your online presence and personal brand are make-or-break. Craft a strong brand image across:

LinkedIn profile - showcase achievements, skills, recommendations

GitHub - displays coding activity and quality through clean repositories

Portfolio website - highlight projects and share valuable content

Social media - post career updates and useful insights, but avoid oversharing

Blogs/videos - demonstrate communication abilities and thought leadership

Online communities - actively engage and build relationships

Ensure your profiles are professional and consistent. Let your technical abilities and potential speak for themselves.

Optimize Your Internship Applications

Applying isn’t enough. You must optimize your internship applications to get a reply:

Ensure you apply to openings that strongly match your profile Customize your resume and cover letters using keywords in the job description

Speak to skills gained from coursework, online learning, and personal projects

Quantify achievements rather than just listing responsibilities

Emphasize passion for technology and fast learning abilities

Ask insightful questions that show business understanding

Follow up respectfully if you don’t hear back in 1-2 weeks

Show interest in full-time conversion early and often

Apply early since competitive openings close quickly

Leverage referrals from your network if possible

This is how you do apply meaningfully. If you want a good internship, focus on the quality of applications. The hard work will pay off.

Succeed in Your Remote Internship

The hard work pays off when you secure that long-awaited internship! Continue standing out through the actual internship by:

Over Communicating in remote settings - proactively collaborate

Asking smart questions and owning your learning

Finding mentors and building connections remotely

Absorbing constructive criticism with maturity

Shipping quality work on or before deadlines

Clarifying expectations frequently

Going above and beyond prescribed responsibilities sometimes

Getting regular feedback and asking for more work

Leaving with letters of recommendation and job referrals

When you follow these tips, you are sure to succeed in your remote internship. Remember, soft skills can get you long ahead in the company, sometimes core skills can’t.

Conclusion

With careful preparation, tenacity, and a passion for technology, you will be able to get internships jobs in USA that suit your needs in the thriving IT sector.

Use this guide to build the right skills, create an impressive personal brand, ace the applications, and excel in your internship.

Additionally, you can browse some good job portals. For instance, GrandSiren can help you get remote tech internships. The portal has the best internship jobs in India and USA you’ll find. The investment will pay dividends throughout your career in this digital age. Wishing you the best of luck! Let me know in the comments about your internship hunt journey.

[gradsiren]

How To Get An Online Internship In the IT Sector (Skills And Tips)

Internships provide invaluable opportunities to gain practical skills, build professional networks, and get your foot in the door with top tech companies.

With remote tech internships exploding in IT, online internships are now more accessible than ever. Whether a college student or career changer seeking hands-on IT experience, virtual internships allow you to work from anywhere.

However, competition can be fierce, and simply applying is often insufficient. Follow this comprehensive guide to develop the right technical abilities.

After reading this, you can effectively showcase your potential, and maximize your chances of securing a remote tech internship.

Understand In-Demand IT Skills

The first step is gaining a solid grasp of the most in-demand technical and soft skills. While specific requirements vary by company and role, these competencies form a strong foundation:

Technical Skills:

>> Proficiency in programming languages like Python, JavaScript, Java, and C++ >> Experience with front-end frameworks like React, Angular, and Vue.js >> Back-end development skills - APIs, microservices, SQL databases >> Cloud platforms such as AWS, Azure, Google Cloud >> IT infrastructure skills - servers, networks, security >> Data science abilities like SQL, R, Python >> Web development and design >> Mobile app development - Android, iOS, hybrid

Soft Skills:

>> Communication and collaboration >> Analytical thinking and problem-solving >> Leadership and teamwork >> Creativity and innovation >> Fast learning ability >> Detail and deadline-oriented >> Flexibility and adaptability

Obtain Relevant Credentials

While hands-on skills hold more weight, relevant academic credentials and professional IT certifications can strengthen your profile. Consider pursuing:

>> Bachelor’s degree in Computer Science, IT, or related engineering fields. >> Internship-specific courses teaching technical and soft skills. >> Certificates like CompTIA, AWS, Cisco, Microsoft, Google, etc. >> Accredited boot camp programs focusing on applied skills. >> MOOCs to build expertise in trending technologies like AI/ML, cybersecurity. >> Open source contributions on GitHub to demonstrate coding skills.

The right credentials display a work ethic and supplement practical abilities gained through projects.

Build An Impressive Project Portfolio

Nothing showcases skills better than real-world examples of your work. Develop a portfolio of strong coding, design, and analytical projects related to your target internship field.

>> Mobile apps - publish on app stores or use GitHub project pages >> Websites - deploy online via hosting services >> Data science - showcase Jupyter notebooks, visualizations >> Open source code - contribute to public projects on GitHub >> Technical writing - blog posts explaining key concepts >> Automation and scripts - record demo videos

Choose projects demonstrating both breadth and depth. Align them to skills required for your desired internship roles.

Master Technical Interview Skills

IT internship interviews often include challenging technical questions and assessments. Be prepared to:

>> Explain your code and projects clearly. Review them beforehand. >> Discuss concepts related to key technologies on your resume. Ramp up on fundamentals. >> Solve coding challenges focused on algorithms, data structures, etc. Practice online judges like LeetCode. >> Address system design and analytical problems. Read case interview guides. >> Show communication and collaboration skills through pair programming tests. >> Ask smart, well-researched questions about the company’s tech stack, projects, etc.

Schedule dedicated time for technical interview practice daily. Learn to think aloud while coding and get feedback from peers.

Show Passion and Curiosity

Beyond raw skills, demonstrating genuine passion and curiosity for technology goes a long way.

>> Take online courses and certifications beyond the college curriculum >> Build side projects and engage in hackathons for self-learning >> Stay updated on industry news, trends, and innovations >> Be active on forums like StackOverflow to exchange knowledge >> Attend tech events and conferences >> Participate in groups like coding clubs and prior internship programs >> Follow tech leaders on social media >> Listen to tech podcasts while commuting >> Show interest in the company’s mission, products, and culture

This passion shines through in interviews and applications, distinguishing you from other candidates.

Promote Your Personal Brand

In the digital age, your online presence and personal brand are make-or-break. Craft a strong brand image across:

>> LinkedIn profile - showcase achievements, skills, recommendations >> GitHub - displays coding activity and quality through clean repositories >> Portfolio website - highlight projects and share valuable content >> Social media - post career updates and useful insights, but avoid oversharing >> Blogs/videos - demonstrate communication abilities and thought leadership >> Online communities - actively engage and build relationships

Ensure your profiles are professional and consistent. Let your technical abilities and potential speak for themselves.

Optimize Your Internship Applications

Applying isn’t enough. You must optimize your internship applications to get a reply:

>> Ensure you apply to openings that strongly match your profile >> Customize your resume and cover letters using keywords in the job description >> Speak to skills gained from coursework, online learning, and personal projects >> Quantify achievements rather than just listing responsibilities >> Emphasize passion for technology and fast learning abilities >> Ask insightful questions that show business understanding >> Follow up respectfully if you don’t hear back in 1-2 weeks >> Show interest in full-time conversion early and often >> Apply early since competitive openings close quickly >> Leverage referrals from your network if possible

This is how you do apply meaningfully. If you want a good internship, focus on the quality of applications. The hard work will pay off.

Succeed in Your Remote Internship

The hard work pays off when you secure that long-awaited internship! Continue standing out through the actual internship by:

>> Over Communicating in remote settings - proactively collaborate >> Asking smart questions and owning your learning >> Finding mentors and building connections remotely >> Absorbing constructive criticism with maturity >> Shipping quality work on or before deadlines >> Clarifying expectations frequently >> Going above and beyond prescribed responsibilities sometimes >> Getting regular feedback and asking for more work >> Leaving with letters of recommendation and job referrals

When you follow these tips, you are sure to succeed in your remote internship. Remember, soft skills can get you long ahead in the company, sometimes core skills can’t.

Conclusion

With careful preparation, tenacity, and a passion for technology, you will be able to get internships jobs in USA that suit your needs in the thriving IT sector.

Use this guide to build the right skills, create an impressive personal brand, ace the applications, and excel in your internship.

Additionally, you can browse some good job portals. For instance, GrandSiren can help you get remote tech internships. The portal has the best internship jobs in India and USA you’ll find.

The investment will pay dividends throughout your career in this digital age. Wishing you the best of luck! Let me know in the comments about your internship hunt journey.