#parallel query performance

[thedbahub]

Navigating MAXDOP in SQL Server 2022: Best Practices for Multi-Database Environments

In the realm of SQL Server management, one of the pivotal settings that database administrators must judiciously configure is the Maximum Degree of Parallelism (MAXDOP). This setting determines the number of processors that SQL Server can use for the execution of a query. Proper configuration of MAXDOP is essential, especially in environments with multiple databases and a limited number of CPU…

[kiryoutann]

Before reading, please check series masterlist to read the warning(s), disclaimer, and to make sure you’re on the right chapter. Minors do NOT interact.

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TRIGGER WARNING: NARCISSISTIC MOTHER, DEPRESSION, emotional abuse, manipulation, cognitive dissonance, undiagnosed mental illness.

My mother was a good mother.

When she walks through the threshold, she carefully places her heavy bag on the table before settling into the chair next to your bed. You're unsure of the reason for her presence or what to say, given how heated things were the last time you spoke. So, you bide your time, waiting for her to speak first, like the obedient daughter she wished you had always been.

You wonder how she feels—if she sees herself mirrored in you. This bears parallels to that day in the past, except the roles are reversed now. You stepped into her shoes, while she takes on the part you once played at the age of eleven, or perhaps twelve.

At that time, you didn’t judge her; you just stared at her, face filled with curiosity and a little bit of sadness. With questions yearned to be satiated. But it remained unspoken—those endless strings of "why." Why did this happen? Why would Mom do this? Why couldn't Mom talk to you? Why didn't Mom tell you anything? Why did Mom want to leave you? The litany of unanswered queries clamored at the tip of your tongue, yet you stubbornly refused to let them slip past your lips.

You wait for her anger, your body gearing up in case she starts to raise her voice. But instead, in an almost hesitant voice, she asks, “Why did you do it?”

The question stops the gears of your mind. You sit there stiffly, waiting for her lashing out—for the usual barrage of insults that typically follow. But instead, what greets you is the sound of a choked-back sob. Hesitantly, you look up and see her head hung low. Like a sorrowful soul.

“Sabrina… she.. she called me,” she managed to say between gasping breaths. “She said you were in the hospital, that the doctor said it was… poisoning.”

Within the four walls of this monotonous room, your mother sobs, tears seeping and painting her jeans dark. However, all you feel is confusion—questions about the authenticity of her sadness because all your life, you’ve known your mother to be a great performer. The last thing you want to be is someone incapable of empathy, but you can’t help the ripe doubt trickling down your throat. You want to be able to choose the person you are; yet, someone has shaped you into a human full of distrust.

“If you had… if you…” Mother lifted her head, trying to regain control of her breathing. “I don’t know what I would have done.”

Hearing that, all you can do is sit with a tense stillness in your spine. Déjà vu. You catch a glimpse of your twelve-year-old self sitting in that very chair, expression blank because Mother hates somberness. In the past, she had too pondered that—of what she would have done. It wasn't just the world's overwhelming vastness that scared you as a young teen. Rather, it was more like the realization that the world would be so dark without a mother. What would you have done if Mom had taken more pills? You didn't want that. You loved her dearly.

“I’m sorry,” is what you manage to say. An apology. An apology for putting her through what she put you through.

Mother's shoulders shook again as she took a quivering breath, tears clumping her eyelashes together—you could almost remember your own state before you arrived here. She took another deep breath, and as she exhaled, her eyes finally settled on you. Instantly, you shrank beneath the weight of her gaze, feeling like an alien—a deformed creature whose shape and form she was criticizing.

“Did it ever cross your mind how much it would hurt me? The shame it would bring our family?”

Shame? Of course. Shame. To have a child so troubled that they would kill themselves. If you ended up dead, there would be whispers, judgments. There would be comparisons. People would talk. They'd wonder what drove you to it, and they'd compare Mother to Joyce in the same way that Mom often compared you to Sabrina. How was it possible for one to be blessedly wedded while the other took her own life?

There was always a search for blame. Had the fault been in how Mother had raised you, leading you down a path towards God's malevolence? Or have you been carrying hamartia since childhood, which has led you to this tragedy?

You remained silent, molars dully digging into the inside of your cheek. You had just survived suicide. Mother had asked if you hadn't considered how much it would hurt her—how much shame it would bring the family, she had said. Repeating it over and over in your mind, you furrowed your brows, feeling like something was off. But, as usual, you swallowed the words on the tip of your tongue and spoke only in a small voice.

“I… I didn’t think about that,” you admitted. “I wasn’t thinking about anyone else.”

It was half a lie, as you did think about Mother at one point—though not in the way she had hoped. She crossed your mind but didn't deter you from swallowing the pills. You didn’t know why, and each time you were unable to rationalize or provide an explanation for whatever you did, you hung your head in shame.

“Why did you do it?”

Why? She repeated her first question, expecting one reason when your "why" was far more complex—it was a tree with roots that had plunged deep into the earth, spreading in every direction, creating a tangled web of intertwining reasons. There was never just one answer to the question "why" for you. When confronted with such demands, you begin to question whether you have taken your 'solitude' for granted all along.

And yet, despite how suffocating it feels under the weight of her stare—your self-consciousness reaching a peak as you worry about the outline of your face, your anxiety about her opinion of your features (the possibility of her commenting how your nose is not “small enough” like hers), your unease that she will point out any perceived imperfections in your skin, and your fear and hyper-awareness of maintaining an acceptable expression to avoid disapproval—she is the only one who bothers to visit you in this foreign place.

Mother had come all the way here from San Francisco, had dropped everything, just to be by your side – the disobedient daughter who failed to live up to her expectations.

“Did you do it for attention?”

The accusation should have stung, should have filled you with indignation. You had been desperately grasping at any way to make yourself feel better before you attempted. You had tried to shut your eyes and will yourself to sleep before you got up and popped every pill you could find into your mouth and chased them all down with alcohol. There were several reasons why you did it, but the primary one was because you were lonely. Alone. You didn’t hesitate to leave anyone because you had no one left.

You had no one left, so you never considered expecting anyone's attention. That night, you just wanted to die.

“Of course not.” you answered without hesitation, quick and certain. Yet, when you lifted your gaze to meet her eyes, a sudden flicker of doubt crossed your mind.

Did you do it for attention?

Despite knowing you didn't, what if it looks that way to others? What can you do to change a mind that isn't yours? The nagging thought of being misunderstood gnawed at you, fueling your frustration and annoyance. Which part of you makes them perceive you this way? Is every perception they have of you who you truly are? Like a spineless reptile, you long to shed your skin, to become someone new—to redefine yourself and escape the allegations people placed on you.

Alas, no opportunity presents itself. You are forever bound by the perceptions of others, and with time, the line between who you truly are and the misinterpreted image of yourself becomes increasingly impossible to distinguish.

“Why did you do it?” She repeated the same question but never begged for an answer. Your mother kept her ego intact. It sounded more like a demand—this was more like her. Demanding, never begging. “Was it because of that man?”

You pause for a moment. “I was just tired.”

The words sound hollow but familiar, like a mimicry of a scene from the past. Mother had uttered something similar once, when it was you sitting in the hospital chair, staring at her pale face after a near-death experience of her own making. You wonder if she remembers it—if it left the same impact on her as it did on you, or if it was simply another Wednesday in December. Your roles were ironically reversed; did she realize this?

“Was it because of him?”

Like Mother’s other questions, there is repetition. It makes you wonder – was it out of genuine concern, or was there something she wanted to prove?

How would she feel about your answer? It's almost as if there's a common theme that binds the women in your family, passed down to you, from your mother, from her mother—a lineage of suffering that seems to revolve around the men. Would her heart ache at the thought of her daughter following in the doom of her predecessors? A paradox of contrasts and twins.

Or… will your mother feel a twisted sense of vindication? Will she look at you and say, “I told you so,” with her all-knowing stare and a smug smirk? That you are here because you ignored the warnings she repeatedly demanded you remember. Even now, you don’t know which is worse: being pitied or being cursed.

Fortunately, unlike your mother, you do not like repeating yourself, nor do you intend to meet her gaze. The silence stretches between you in this hospital room. Your mother, out of the kindness of her heart, allows it. Another déjà vu descends upon you. You remember very well how your conversation with Mother ended that day—on a Wednesday in December. That role-reversed version.

You lied a tremendous deal to the psychiatrist. Do you regret it? Of course, you do. Would you do it again given the chance? Most likely. Throughout the entire session, you waited for her to call out every lie you told, but it seems that psychiatrists don't possess polygraphs in their minds. You should feel relieved. Yet, you know that each unchecked lie is another burden you must carry with you for life.

When you came out of the office, Mother was still sitting in the chair where she had been waiting for you. She asked you a few questions, and while you didn't answer them all, you did tell her that everything was alright. Satisfied, the two of you walked the sidewalks of the city once more, beneath the somber, cloudy London sky.

“What should we have for dinner tonight?”

It seemed almost surprising when the question effortlessly rolled off your tongue, sounding more natural and lighter than it had in the previous days. On the day of your discharge from the hospital, the conversation between you and Mother felt unusually stiff and guarded—at least on your part. There were no arguments, but a palpable tension hung in the air, heavy and suffocating, filled with unspoken expectations and hidden demands.

Now, it felt like no time had passed. As if you were simply picking up where you had left off, back in the days when your relationship had been strained but still intact.

“Well, I was going to cook something for you, but it’s getting rather late,” she replied. “I know there’s nothing in your fridge, so we should probably go out and grab something instead.”

“Where do you want to go?”

Mother shrugged. “I don’t know. You know this city better than I do. You choose.”

“What are you in the mood for?”

“Anything is fine.”

You settle on the first Italian restaurant you pass by. Despite not being the fanciest establishment, the casual ambiance and soothing jazz music mixed with the chatter of other patrons create a cozy vibe that draws you in. One of the four walls is painted in cool crimson, adorned with black-and-white photographs and a few framed sepia-toned prints that hint at the restaurant's family-run history.

The waitress who tended your table jotted down your orders and whisked away, leaving you alone to wait for your food. Leaving you alone with your mother.

This, you realize, is the closest you’ve been to her after a long time. Throughout her visit, you spent the majority of your time together in the hospital ward, with nurses constantly entering and leaving the room. Once you were discharged, Mother spent her time meticulously cleaning your apartment to her specific standards, while you avoided conversation by switching on the television and keeping her occupied with her favorite shows.

In the air floated the combined aroma of garlic, tomatoes, and hot oil. As you looked up at your mother, you found her sweeping a critical gaze around the surroundings. Resting her head on her hand, you spotted the fine details you had never noticed before: the gold ring on her middle finger, her medium-length, almond-shaped nails painted a deep red, the pronounced structure of her digits, and her wrinkled knuckles. The dichotomy between how she seemed exactly the same as you remembered and yet bore changes underscored the silent distances that have grown between you.

Mother's gaze drifted to the window. “This place is so depressing,” she muttered.

You gaze in the direction of the object of her observation, searching for clues about what prompted her assessment. Is she referring to the cafe, the street outside, the city as a whole, or the table where you both sit? As you search for answers, you're also desperately searching for a positive quality to highlight so she gives this place a chance, so she doesn't contemplate packing her things and bolting out the door as she has done before.

Turning her attention back to you, she said, “It’s better back home.”

Thinking she was talking about your apartment, you had to disagree. Despite spending most of your time at home, you'd rather be anywhere than there.

“There’s a place like this in Polk Gulch. You know, the ones with the stars, what is it called…” She made a vague gesture with her hands, searching for the word.

“Michelin stars?”

“Yes, exactly. The ones with the Michelin stars. Now, those are the kinds of places we should be going to. Back home.”

The word was received in a strange way by you, but you did not comment. Mother read this as another turn for her to continue the conversation.

“Your favorite places are still there, you know,” she said. "Don't you miss it?"

“Sometimes,” you admit quietly. And it’s true. There are nights when you think about San Francisco, about the places you used to visit—places you grew up in and some fond memories they hold. However, there are also the seeds of something rotten there, ones that you know may find you even in your dreams.

San Francisco, the city of your attempted self-dismantlement. Your attempt to strip away all that you were and repair the creation you have become. It failed miserably, so you fled to London. For a new beginning, for a new you.

And yet, somewhere along the way, you’ve inadvertently turned London into a second San Francisco. What should have been a fresh start has now turned into an echoing cycle. The same demons you sought to escape from have followed you here, infusing their putrid influence into the foundation of your carefully constructed dream life. Now, you're unsure how to salvage any of it.

Sometimes. Your mother cocked a brow, her expression unreadable except for the downward tug at the corners of her lips. Before she could say anything else, the waitress placed your orders down in front of you, the aroma of Italian cuisine wafting across the table. You hoped the food would be good enough for her.

Mother was unusually chatty on the way home, words flowing freely in a pleased tone. It must have been the wine that the server had offered to you both. At first, you expected Mother to decline, as she often does, muttering about the harm of alcohol once the man was out of earshot. However, it took you by surprise when she accepted it without question, and you wondered whether she had changed.

The feeling of lightness envelops you, even in the presence of unfamiliarity, as you listen to your mother chat away. You exhaled as Mother continued to talk about whatever, each laugh and random comment eroding the tension that had been weighing on your shoulders. The heaviness that once defined your interactions now dissolves into fission. She sounded like somebody new, and you treat her like somebody you don’t need to tiptoe around.

“It wasn’t bad,” she said, talking about the food.

You tucked your chilly hand into the warm protection of your coat pockets as you gazed at the ever-glistening street and the passing cars. “It was good,” you replied.

 “Well, I wouldn’t say it was that good, but it certainly wasn’t bad.”

The two of you continue walking, and as you make another turn, the opera building's well-known shape comes into view. Your heart clenches in panic, realizing you have been unconsciously leading yourself down the path you always take when returning home from rehearsals. Not wanting to draw attention to it, you remain silent, but your efforts are in vain as Mother quickly notices the distinctive neoclassical structure.

“Isn’t that your ballet place?” she asked, her manicured finger pointed at the building.

“Yes,” you replied simply, hoping she would look elsewhere before she sees it, before—

“Is that you?” she asks, not taking her eyes off the large illuminated poster in the front window—the bold-lettered title “Swan Lake,” and your own face staring back at you, radiant and poised like a girl who has earned her place in the world.

“Yes,” you reply, throat constricting as if bracing for something.

But instead of whatever you were expecting, your mother's expression shifted, the crease in her forehead accentuated as she turned to face you. “Why didn’t you tell me about this?” She asked, and the hurt in her tone took you aback.

Before you could formulate a response, she had already crossed the street, so determined that she didn't bother looking both ways. She headed straight toward the poster, the click-clack of her heels on the street accompanied by the howl of the wind. You hesitated for a moment, but ultimately decided to trail after her, heart pounding in your chest.

You watched your mother pull out her phone, snapping a few quick photos of the poster. She then held the phone close to her chest, gazing at the image wordlessly. You wondered what was going through her mind. Was she proud? Disappointed? Indifferent? Your mind replayed the last discussion you both had on your ballet career.

“This is… this is something,” she said—the click of her heels sounded again as she took quick, tiny steps toward you. “You made it! The lead role!”

“It’s called the Swan Queen, the role.”

"The Swan Queen…" she echoed, turning back to the poster, the silver light reflecting on it allowing you read her expression more clearly—a proud smile stretched across her face. Proud. “I always knew you had it in you. See? I know you so well; you’re my daughter after all. It’s a good thing I brought you to that ballet class when you were a little girl, isn’t it?”

You let out a chuckle, feeling the warmth spread from your sternum.

To your further surprise, your mother reached out and cupped your cheeks, aligning her eyes to meet yours with a tenderness you nearly forgot she was capable of.

“Oh, my little girl,” she murmured.

And you… suddenly want your mother again, and hope for her to want you back. You remember the times when you both sang to your favorite songs in the car, driving under the iconic Golden Gate Bridge as the dying sun from the west caressed Mom's crow's feet. Belting out something of Mariah Carey, although not quite matching the skill of the original singer, but making up for it with an equal amount of enthusiasm and love.

(I will never be anything without Mom.)

In the present day, you find yourself leaning into her palm, like a fawn finding its loving mother. Your past arguments seem so far in the past, and you are big-hearted women forgiving each other and creating excuses to keep this moment lasting. Perhaps somewhere in those past conversations, you had overreacted, or you weren't good at understanding her words.

Because your mother would never intend to hurt you. She is a good mother.

@strawberrygato @aprosiacperson @chipsbuttercream @arrozyfrijoles23 @pastel-devil-06 @rroseskull @olives10 @cricricorner @idrkman @strrynigghts @mims900

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[himanitech]

Wielding Big Data Using PySpark

Introduction to PySpark

PySpark is the Python API for Apache Spark, a distributed computing framework designed to process large-scale data efficiently. It enables parallel data processing across multiple nodes, making it a powerful tool for handling massive datasets.

Why Use PySpark for Big Data?

Scalability: Works across clusters to process petabytes of data.

Speed: Uses in-memory computation to enhance performance.

Flexibility: Supports various data formats and integrates with other big data tools.

Ease of Use: Provides SQL-like querying and DataFrame operations for intuitive data handling.

Setting Up PySpark

To use PySpark, you need to install it and set up a Spark session. Once initialized, Spark allows users to read, process, and analyze large datasets.

Processing Data with PySpark

PySpark can handle different types of data sources such as CSV, JSON, Parquet, and databases. Once data is loaded, users can explore it by checking the schema, summary statistics, and unique values.

Common Data Processing Tasks

Viewing and summarizing datasets.

Handling missing values by dropping or replacing them.

Removing duplicate records.

Filtering, grouping, and sorting data for meaningful insights.

Transforming Data with PySpark

Data can be transformed using SQL-like queries or DataFrame operations. Users can:

Select specific columns for analysis.

Apply conditions to filter out unwanted records.

Group data to find patterns and trends.

Add new calculated columns based on existing data.

Optimizing Performance in PySpark

When working with big data, optimizing performance is crucial. Some strategies include:

Partitioning: Distributing data across multiple partitions for parallel processing.

Caching: Storing intermediate results in memory to speed up repeated computations.

Broadcast Joins: Optimizing joins by broadcasting smaller datasets to all nodes.

Machine Learning with PySpark

PySpark includes MLlib, a machine learning library for big data. It allows users to prepare data, apply machine learning models, and generate predictions. This is useful for tasks such as regression, classification, clustering, and recommendation systems.

Running PySpark on a Cluster

PySpark can run on a single machine or be deployed on a cluster using a distributed computing system like Hadoop YARN. This enables large-scale data processing with improved efficiency.

Conclusion

PySpark provides a powerful platform for handling big data efficiently. With its distributed computing capabilities, it allows users to clean, transform, and analyze large datasets while optimizing performance for scalability.

For Free Tutorials for Programming Languages Visit-https://www.tpointtech.com/

[sciencestyled]

Cleopatra and the Cosmic Comedy: How I Got Sucked into the Great Attractor

It was an ordinary morning in the heart of Alexandria, or so it seemed. My attendants were bustling about, tending to my every need as I reclined on my sumptuous divan, pondering the mysteries of the universe and the peculiar behavior of Romans. Little did I know, this day would mark the beginning of an obsession that would rival even my infamous affairs.

The whole affair began with a rather mundane event: a particularly insistent messenger from the Library of Alexandria, flanked by scrolls and an air of urgency. "Your Majesty," he began, barely containing his excitement, "there's been a celestial discovery of profound significance."

Now, let me tell you, my loyal subjects, it takes a lot to impress a queen who has commanded the Nile and charmed Julius Caesar. But the messenger's fervor piqued my curiosity. I beckoned him to continue.

He unfurled a scroll depicting strange movements of galaxies, arrows pointing toward a mysterious region in the cosmos. "They call it the Great Attractor," he declared. The name itself had a certain charm, much like my own. I was hooked.

"Great Attractor, you say? Sounds like it needs a proper introduction," I mused. "Bring me the finest minds from the Library. We shall investigate this cosmic Casanova."

Soon, my court was abuzz with the arrival of astronomers and scholars, each more eager than the last to present their theories about this celestial phenomenon. The tales they told! Galaxies, those grand whirlpools of stars, being drawn irresistibly towards a singular point in space. It was as if the heavens themselves had a penchant for drama worthy of Cleopatra's court.

As the days passed, I found myself increasingly enchanted by this Great Attractor. It was a force unseen, yet so powerful it could bend the paths of entire galaxies. How deliciously enigmatic! And the parallels to my own life were irresistible. Was I not the Great Attractor of Egypt, drawing all who beheld me into my orbit?

But the story truly took a comedic turn when I decided to consult the oracle. Yes, my dear followers, Cleopatra sought cosmic counsel. The oracle, ensconced in her smoky chamber, took one look at my query and burst into a fit of laughter. "The stars, my queen, are as fickle as lovers. This Great Attractor you seek? It’s the universe’s way of reminding us that even the cosmos cannot resist a bit of chaos and allure."

Her words, though cryptic, struck a chord. The Great Attractor was not just a cosmic curiosity; it was a reflection of my own royal magnetism on a grander scale. How could I not be enthralled?

Determined to delve deeper, I commanded my scholars to write a treatise that would blend our newfound astronomical knowledge with the elegance and wit befitting my court. The resulting scrolls were magnificent, detailing the gravitational pull of this cosmic wonder in a way that even the most mundane Roman senator could understand.

But, alas, the true inspiration for the article you are about to read came from a rather unexpected source—Antony’s insatiable need for theatrics. One evening, during a particularly lavish feast, he challenged me to explain the Great Attractor in the form of a courtly performance. Never one to back down, I took to the stage, weaving a tale of cosmic seduction that left the audience spellbound and, might I add, a little tipsy.

From that night, the idea of documenting the Great Attractor's celestial charms took root. It was a story too enchanting, too delightfully chaotic to be confined to my palace walls. And so, with the fervor of a queen who had once ruled the heart of Rome and the intellect of Egypt, I set about writing the most captivating account of the Great Attractor's irresistible allure.

So, my dear admirers, as you prepare to dive into the celestial seduction that is the Great Attractor, remember this: even the most regal of queens can be captivated by the mysteries of the cosmos. And perhaps, in understanding this astronomical enigma, you might find a reflection of your own gravitation towards the unknown.

Enjoy the cosmic courtship, my beloved subjects. Cleopatra, the original Great Attractor, has spoken.

[ship-of-adramyttium]

Impressions of Artificial Intelligence - Part 3

AI and Machine Learning and The Mystery of Knowledge Read Part 1 here, and Part 2 here.  What We Know Brilliant informational scientists, programmers, computer researchers, and specialists have a profound understanding of the architecture of machine learning models, including LLMs. On the front end, these models are built on well-understood coding protocols. As mentioned before, there are intricate algorithms that weight words and phrases, tokenizing language in order to recognize context. There are parallel networks that perform tasks forward and backward, a bit like memory in a human brain. This is done at lightning speeds, with massive servers running in parallel to one another. A little bit like how brains run multiple inputs at the same time.  It used to be that computer programmers would line up machines in serial systems, meaning one after another, so that one computer would complete a task and would feed into another computer. This would layer inputs until the end of the serial chain for maximum output. This requires massive amounts of energy and relatively long timeframes. Eventually, someone said, what if we run these systems in parallel, next to each other. Then, let's break the main task into smaller tasks running at the same time rather than each task in a line. After all, that is how brains work. Brains are profoundly vast in their ability to retain, maintain, and act on available information at the same time.  Parallel Processing This is called parallel processing and it rapidly speeds up the ability of a system to find answers and generate output. Parallel processing led to the creation of neural networks back in the 1980s. In an AI model, there is a massive amount of information pouring into each channel and then overlapping onto other channels (the same way the human brain does). This requires massive amounts of computing power. We now have massive server farms dedicated to machine learning and AI. The physical architecture and energy needs required for the software architecture for AI is unbelievably vast.  On the back end, at the point of the end user, we know that there is now an explosion of AI uses and bots that can do all sorts of things. Once you have a functional LLM and AI system, you can section off parts to have it do different things. These are called ‘bots’, because they are task specific. A bot to manage your calendar, another to write emails, another to generate images, another to do financials in the background, another to ghostwrite your motivational book, another to do complex math. There are bots that can act as a personage from the past, a smart person in the present, or a therapist.  The GPT Store is a good example of what this looks like. For interaction with topic specific bots, Character.AI is a fascinating exploration of the capability of bots.  More simply, though, all these GPTs (GPT means Generative Pre-trained Transformer) are dependent on how well they communicate with the end user. So very generally, what is known of AIs, on the front end, is the software and hardware architecture needed to create a functional AI. This requires training the AI to understand the context of input queries, access to datasets, and careful programming (the Pre-Trained part of GPT). On the back end, the output to the end user, there are the translation requirements needed for us to read the responses from the AI on our various devices. These front end and back end aspects of AI are very well understood. The people who designed these systems are brilliant, unnamed scientists and programmers who will never be household names.  What We Don’t Know Most of the time, when I am working with AI dialogues and rewrites, I am aware that most of the answers the AI gives me, while drawing on vast knowledge bases, are formulaic, repetitive in a meta sense, and predictable in structure. Part of this is because of the way systems are designed; a programmed system will give programmed-like responses. Part of this predictability is because a lot of human writing is predictable and formulaic.  Sometimes, though, something comes through the AI model that is fantastically creative and even beautiful. The tokens get weighted just right, the predictive algorithm decodes just so, and it is as if you are reading a response written by your closest friend, favorite author, genius brother-in-law. If you get enough of these moments in a row, it can seem like a flash of conscious awareness on the part of the machine. Like magic, a new entity seems to come into being through words and image put together in a truly creative way.  Through both word and image, the advancements in AI are explosive. Every week seems to bring a new, amazing outcome. For instance, check out the incredible output from the text-to-video AI model named Sora. This is just one example of many across domains and uses. It is hard to keep up.  Transformer Architecture LLMs, for the most part, work in a ‘transformer architecture’. Transformers (the “T” in ChatGPT, for instance) are a structure of encoders, which receive information - prompts from users and input from the dataset, and decoders, which impart information - responses from the model to the end user.  Imagine that on one side of an LLM there is a rising road that reaches all the way up to the top of a mountain. This road is called Encoder. On the other side, there is a descending road all the way to the bottom of the mountain. This road is called Decoder.  The Invisible Bridge The reason the road doesn’t have the same name from start to finish is because right at the top of the mountain, there is an enormous gap between the highest point of the Encoder road and the beginning of the descent down the Decoder road. People have told you to just drive across because the bridge between the Encoder road and the Decoder road is invisible, but is still there. 

No one knows who built the bridge. No one even knows, for that matter, why the bridge is there. Sometimes, in the middle of the invisible bridge, things go weird. You see things, hear things, read things that are not normal. Most of the time, though, things just cross over like a normal bridge over any other canyon or abyss.  This bridge is what we don’t know in AI systems. Something happens on the bridge between encoding and decoding that allows for an AI model to put things together in creative, structured ways that can defy explanation, in good ways and in not so good ways.  Liminal AI A lot of AI work, now that the internet has been ‘scraped’ for all knowledge, is the hard work of training AIs to discern misinformation from information, truth from fantasy. This is the work I do. So are thousands of people around the world. The other reason the work in AI is refocusing now is because no one really knows how these systems work.  The gap, the invisible bridge, between encoding and decoding is an unknown in-between, a liminal space where formerly binary systems, 1s and 0s acting as ‘yes’ and ‘no’ gates, are now probability matrices. This means the information is sometimes ‘yes’ and ‘no’ at the same time. The work an AI model does on the invisible bridge is…their own thing.  The transformers in AIs are like a black box, the phrase used in science for where things happen that we don’t fully understand. So a big part of the work in AI advancement and development is reverse-engineering why they do what they do and act like they do. The people who created AI are now spending a good portion of their time trying to figure out how their creation works.  How Does This Thing Work? Before everyone totally freaks out, it is worth remembering there are many things we have created or discovered that we do not understand why they work. Airplanes work, but it took a fair bit of time to really understand why. Electricity works, but it took a long time to understand what it actually does. Flight and electricity are actually incredibly complex operations, and the explanations confuse people. The fact that we have both in our daily lives doesn’t mean we understand what is happening.   My favorite is gravity: we know what gravity does and even how it acts. Based on our crude understanding of gravity, we can launch people into space, predict asteroids, and the orbits of planets. But what it is, where it is, why it is, is still a mystery.  Antibiotics are another one. For a long time, we knew that penicillin and other sulfur based antibiotics worked really well, but didn’t know why. We understand much more now. But even so, we can create them and even target them to specific bacteria, but there are whole aspects of why they work we still do not understand. We still take them, though, because their final effect is to kill the harmful bacteria making us sick.  We are currently in what is called the “psychedelic renaissance”, where research into psychedelic medicines is exploding. The great secret of psychedelics is the same as AI. We know, for instance, what psilocybin (magic mushrooms) does right up to the point it interacts with serotonin receptors in the brain and body. And then we know very little. They appear to help with all kinds of mental health issues, and are powerful agents for creativity and insight. Therein lies the mystery. Like gravity, we know what psychedelics do, but really don’t know why they do what they do.  More Questions Mean The Right Path Each of these examples, however, are the essence of science. Answers in science are really only platforms for more and better questions. Answers in science are temporary and provisional. The formula of science is this: The more we know, the more we realize we don’t know. Discoveries are what lead to new discoveries. Even though computer science has been around for almost 200 years, we are now just at the beginning of this new aspect of artificial intelligence with transformers, encoder-decoder architecture, and the public participation in the technology.  The other secret to science, which should be public knowledge, is that good questions come from good evidence. And good answers build on good science. This is why trusting scientific experts when we don’t know the science leads us to better outcomes. By close observation and our own rigorous study, we learn to ask better questions of experts in their fields, rather than simply questioning expertise.  We go up a road, the creation of an artificial intelligence, and understand the construction and direction of the road pretty well. We come down the road in the proliferation of LLMs and chatbots and see how the thing works in practical application. But in between the road up and the road down, we have to cross the invisible bridge of the mountain we have built. AI is a technology that is exploding. This is where the new discoveries and possibilities wait. Let's ask really good questions about what we are doing with it. Read the full article

[rishiaca]

ChatGPT and Machine Learning: Advancements in Conversational AI

Introduction: In recent years, the field of natural language processing (NLP) has witnessed significant advancements with the development of powerful language models like ChatGPT. Powered by machine learning techniques, ChatGPT has revolutionized conversational AI by enabling human-like interactions with computers. This article explores the intersection of ChatGPT and machine learning, discussing their applications, benefits, challenges, and future prospects.

The Rise of ChatGPT: ChatGPT is an advanced language model developed by OpenAI that utilizes deep learning algorithms to generate human-like responses in conversational contexts. It is based on the underlying technology of GPT (Generative Pre-trained Transformer), a state-of-the-art model in NLP, which has been fine-tuned specifically for chat-based interactions.

How ChatGPT Works: ChatGPT employs a technique called unsupervised learning, where it learns from vast amounts of text data without explicit instructions or human annotations. It utilizes a transformer architecture, which allows it to process and generate text in a parallel and efficient manner.

The model is trained using a massive dataset and learns to predict the next word or phrase given the preceding context.

Applications of ChatGPT: Customer Support: ChatGPT can be deployed in customer service applications, providing instant and personalized assistance to users, answering frequently asked questions, and resolving common issues.

Virtual Assistants: ChatGPT can serve as intelligent virtual assistants, capable of understanding and responding to user queries, managing calendars, setting reminders, and performing various tasks.

Content Generation: ChatGPT can be used for generating content, such as blog posts, news articles, and creative writing, with minimal human intervention.

Language Translation: ChatGPT's language understanding capabilities make it useful for real-time language translation services, breaking down barriers and facilitating communication across different languages.

Benefits of ChatGPT: Enhanced User Experience: ChatGPT offers a more natural and interactive conversational experience, making interactions with machines feel more human-like.

Increased Efficiency: ChatGPT automates tasks that would otherwise require human intervention, resulting in improved efficiency and reduced response times.

Scalability: ChatGPT can handle multiple user interactions simultaneously, making it scalable for applications with high user volumes.

Challenges and Ethical Considerations: Bias and Fairness: ChatGPT's responses can sometimes reflect biases present in the training data, highlighting the importance of addressing bias and ensuring fairness in AI systems.

Misinformation and Manipulation: ChatGPT's ability to generate realistic text raises concerns about the potential spread of misinformation or malicious use. Ensuring the responsible deployment and monitoring of such models is crucial.

Future Directions: Fine-tuning and Customization: Continued research and development aim to improve the fine-tuning capabilities of ChatGPT, enabling users to customize the model for specific domains or applications.

Ethical Frameworks: Efforts are underway to establish ethical guidelines and frameworks for the responsible use of conversational AI models like ChatGPT, mitigating potential risks and ensuring accountability.

Conclusion: In conclusion, the emergence of ChatGPT and its integration into the field of machine learning has opened up new possibilities for human-computer interaction and natural language understanding. With its ability to generate coherent and contextually relevant responses, ChatGPT showcases the advancements made in language modeling and conversational AI.

We have explored the various aspects and applications of ChatGPT, including its training process, fine-tuning techniques, and its contextual understanding capabilities. Moreover, the concept of transfer learning has played a crucial role in leveraging the model's knowledge and adapting it to specific tasks and domains.

While ChatGPT has shown remarkable progress, it is important to acknowledge its limitations and potential biases. The continuous efforts by OpenAI to gather user feedback and refine the model reflect their commitment to improving its performance and addressing these concerns. User collaboration is key to shaping the future development of ChatGPT and ensuring it aligns with societal values and expectations.

The integration of ChatGPT into various applications and platforms demonstrates its potential to enhance collaboration, streamline information gathering, and assist users in a conversational manner. Developers can harness the power of ChatGPT by leveraging its capabilities through APIs, enabling seamless integration and expanding the reach of conversational AI.

Looking ahead, the field of machine learning and conversational AI holds immense promise. As ChatGPT and similar models continue to evolve, the focus should remain on user privacy, data security, and responsible AI practices. Collaboration between humans and machines will be crucial, as we strive to develop AI systems that augment human intelligence and provide valuable assistance while maintaining ethical standards.

With further advancements in training techniques, model architectures, and datasets, we can expect even more sophisticated and context-aware language models in the future. As the dialogue between humans and machines becomes more seamless and natural, the potential for innovation and improvement in various domains is vast.

In summary, ChatGPT represents a significant milestone in the field of machine learning, bringing us closer to human-like conversation and intelligent interactions. By harnessing its capabilities responsibly and striving for continuous improvement, we can leverage the power of ChatGPT to enhance user experiences, foster collaboration, and push the boundaries of what is possible in the realm of artificial intelligence.

[xaltius]

Are Data Centers in a Tight Spot to Manage Gen-AI Workloads?

Generative AI (Gen-AI) has exploded onto the scene, creating content, writing code, and answering complex queries with astonishing fluency. But behind every compelling AI-generated image or intelligent chatbot response lies a massive, often unseen, infrastructure: the data center. The fundamental question looming for these digital powerhouses is: Are data centers in a tight spot to manage the insatiable demands of Gen-AI workloads?

The short answer is: Yes, they are, but they're rapidly evolving to meet the challenge.

Gen-AI models are not your average workload. They possess unique characteristics that push the limits of existing data center capabilities in ways traditional enterprise applications never did.

The Unprecedented Demands of Generative AI

Compute Intensity Beyond Compare: Training cutting-edge large language models (LLMs) and diffusion models requires astronomical amounts of computational power. We're talking about billions, even trillions, of parameters that need to be trained over weeks or months, demanding thousands of specialized processors like GPUs (Graphics Processing Units) working in tandem. This isn't just "more compute"; it's a different kind of compute, optimized for parallel processing.

Power Consumption Soaring: All that compute translates directly into monumental energy consumption. A single rack of GPUs can consume as much power as an entire small office building. Scaling this to hundreds or thousands of racks places immense strain on a data center's power infrastructure, requiring new levels of grid connection, power distribution units (PDUs), and uninterruptible power supplies (UPS).

The Cooling Conundrum: More power means more heat. Traditional air-cooling systems, while effective for standard servers, often struggle to dissipate the concentrated heat generated by dense GPU clusters. Overheating leads to performance degradation and hardware failure, making advanced cooling solutions (like liquid cooling) a necessity, not a luxury.

Network Bandwidth Bottlenecks: Training massive distributed models requires constant, high-speed communication between thousands of GPUs. This demands ultra-low latency, high-bandwidth interconnects within the data center, often pushing beyond standard Ethernet speeds and requiring specialized networking technologies like InfiniBand or custom high-speed fabrics. Data movement within the cluster becomes just as critical as compute.

Data Volume and Velocity: Generative AI models are trained on petabytes of data – text, images, audio, video. Storing, accessing, and rapidly feeding this data to training pipelines puts significant pressure on storage systems and data transfer rates.

How Data Centers Are Adapting (or Need To)

To avoid being in a perpetual tight spot, data centers are undergoing a radical transformation:

GPU-Centric Design: New data centers are being designed from the ground up around GPU clusters, optimizing power, cooling, and networking for these specific compute requirements.

Advanced Cooling Solutions: Liquid cooling (direct-to-chip, immersion cooling) is moving from niche to mainstream, as it's far more efficient at removing heat directly from the processors.

High-Bandwidth Networking: Investing in next-generation optical interconnects and specialized network architectures to ensure data flows freely between compute nodes.

Energy Efficiency & Renewables: A strong push for greater energy efficiency within the data center and increased reliance on renewable energy sources to power these energy-hungry workloads.

Modular and Scalable Designs: Building data centers with modular components that can be rapidly scaled up or down to accommodate fluctuating AI demands.

Edge AI Workloads: For inference and smaller models, pushing AI computation closer to the data source (edge computing) can reduce latency and bandwidth strain on centralized data centers.

While the demands of Generative AI are indeed putting data centers in a tight spot, it's also a powerful catalyst for innovation. The challenges are significant, but the industry is responding with fundamental architectural shifts, pushing the boundaries of what's possible in compute, power, and cooling. The future of AI relies heavily on these unseen giants successfully adapting to the new era of intelligence.

[nominalappraisal]

special as if to discern

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[thedbahub]

Supercharging SQL Server Query Performance with Trace Flag 8649

Introduction Have you ever found yourself staring at your screen, drumming your fingers impatiently while waiting for a SQL Server query to finish? Slow query performance can be incredibly frustrating, especially when you’re under pressure to get results fast. But fear not, dear reader! Today, I’m going to share a powerful technique that can supercharge your query performance and make those…

[forblogmostly]

Cellecor Gadgets Clarifies NSE Filing Oversight, Reinforces Commitment to Compliance

In a recent communication to the National Stock Exchange of India, Cellecor Gadgets Limited, formerly known as Unitel Info Limited, issued an official clarification regarding the submission of its audited financial results for the financial year ending March 31, 2025. The correspondence was prompted by a query from the Listing & Compliance Department of the NSE, seeking explanations surrounding the filing process and data integrity.

Ravi Agarwal, Managing Director of Cellecor Gadgets, addressed the matter with transparency and diligence. He confirmed that the board meeting to approve the annual financials was held and concluded at 1:15 PM on April 17, 2025. Immediately after the meeting, the company uploaded the audited results on the NSE platform at 1:32 PM the same day. However, an inadvertent lapse occurred when the results were uploaded under the “Board Meeting” tab instead of the designated “Financial Results – Announcement” section of the NEAPS portal. Recognizing this mistake, the company acted promptly to rectify the issue and re-submitted the correct files under the proper tab by 7:20 PM on the same evening. Acknowledgment copies for both submissions were attached to the clarification as Annexures A and B respectively.

Additionally, Cellecor Gadgets acknowledged a discrepancy between the XBRL and PDF versions of the submitted financial statements. The mismatch, which arose due to an error during the XBRL filing process, was immediately addressed. A corrected XBRL file was prepared and shared with the exchange to ensure consistency and transparency. Notably, the figures presented in the PDF version of the financials remained unchanged and accurate as per the board’s approved outcome.

The company also reiterated its commitment to maintaining accuracy and compliance in all future disclosures. The communication emphasized that steps were already being taken internally to strengthen their review mechanisms to avoid a recurrence of similar errors. Management expressed sincere regret for any inconvenience the oversight may have caused to the regulators and stakeholders and requested the NSE to take the revised submissions on record.

In parallel, the independent audit report issued by Ambani & Associates LLP validated the integrity of the financial results. The auditors confirmed that the standalone financial statements for FY 2024-25 were prepared in line with regulatory standards, including those outlined in Regulations 33 and 52 read with Regulation 63(2) of the SEBI (LODR) Regulations, 2015. The report concluded that the company’s financials present a true and fair view of its financial position in accordance with applicable Indian accounting standards.

The auditors conducted their review in adherence to the Standards on Auditing issued under Section 143(10) of the Companies Act, 2013. They highlighted that the audit was performed with independence and objectivity and that appropriate evidence was obtained to support their opinion. Their scope included a thorough assessment of internal controls, accounting policies, risk evaluation, and overall financial reporting integrity. Importantly, no material misstatements were identified, and the company’s ability to continue as a going concern was affirmed.

Through this comprehensive clarification, Cellecor Gadgets Limited not only addressed the NSE’s concerns but also showcased its proactive stance on compliance and corporate governance. The firm’s swift corrective actions and transparent disclosures reflect a commitment to uphold the standards expected of a publicly listed company. As it continues to evolve in a dynamic tech landscape, Cellecor reassures its investors and regulators alike of its dedication to operational transparency, financial accuracy, and ethical responsibility.

[kamalkafir-blog]

Principal Database Engineer - Golden Gate

Job title: Principal Database Engineer – Golden Gate Company: Oracle Job description: , Data Guard, Corruption, Backup and Recovery, RMAN, Performance, Memory Management, Parallel query, Query tuning, Storage…, ASM, Security, Networking, Enterprise Manager etc. Career Level – IC4 Responsibilities: Minimum of 10 years working… Expected salary: Location: India Job date: Wed, 16 Apr 2025 06:35:02…

[fromdevcom]

HBase is the Hadoop Database, which is a NoSQL database running on top of Hadoop. HBase combines the Hadoop scalability by running on the HDFS (Hadoop Distributed File System) with real-time data.  In this article, Hadoop and big data professionals are introducing HBase and the major reasons behind its popularity. What is HBase? HBase is one of the open source distributed databases designed to store record-oriented data throughout a scalable machine cluster. Professionals refer HBase as a “sparse, distributed, consistent, persistent, multi-dimensional, sorted map.” Didn’t get it? Let explain a bit – Sparse – If a row has null value in a column, it doesn’t take space Distributed – rows are spread across several machines  Consistent – It is strongly consistent  Persistent – HBase stores data on disk with a log, hence it sticks around Sorted – All the rows in HBase are stored in sorted order so that user can seek them faster Multi-dimensional – Data stored in HBase is addressable in several dimensions- rows, tables, columns, versions, etc.  Why companies are using NoSQL store even if they have a relational database? We are not saying that relational database is useless. In fact, relational databases are terrific offering killer features- Ability to decompose the physical data storage into different conceptual buckets Modify the state of many related values atomically.  Salesforce is heavily dependent on the relational database. But then there is a subset of use cases that include unique requirements for relational data. Less emphasis on relationship webs that need complex transactions for correctness; and more emphasis on large data streams that accrue over time, and require linear access uniqueness.  Companies can store these in RDBMS. However, when they do, they pay a penalty (of scale and performance limitations) for features they don’t require. For those new use cases, HBase has been added to their toolkit. HBase can leverage the distributed processing paradigm available in HDFS. It can host large tables with billions of rows with millions of columns and run all over a cluster of commodity hardware. HBase is a robust and sturdy database that takes help of MapReduce to combine real-time query capabilities with value store speed and batch processing. In simple words, with HBase, companies can make a query for individual records and obtain aggregate analytic reports. Scalability – HBase supports scalability in both modular and linear format Sharding – Sharding of tables is supported by HBase. It is also configurable. Consistency – HBase also supports consistent read and write operations  Distributed storage – Distributed storage like HDFS is supported by HBase Failover support – HBase supports automatic failover  API support - Java APIs are supported by HBase  Backup support - Backup support for Hadoop MapReduce jobs in Hbase tables is available in HBase. MapReduce support – MapReduce support is available for parallel processing of bulk data Real-time processing – HBase supports block cache and Bloom filters to make real-time processing easy.  HBase is different from a relational database and needs a unique approach to modeling the data. It defines a four-dimensional data model and the below four coordinates explain each cell – Row key – Each row contains a unique row key. The row key doesn’t include data type and is treated internally as a byte array. Column family – row data is organized within column families; each row has the same set of column families, however across rows, the same column families don’t require the same column qualifiers. HBase stores column families within own data files.  Column qualifier – Column families explain actual columns known as column qualifiers.  Version – Each column can have configurable version numbers. You can access the data for certain version of a column qualifier.  Why HBase is the foremost choice of all the NoSQL stores? Choosing HBase is a key area of investment.

There are three factors that influence the decision making – HBase is a strongly consistent store – HBase is a CP store and not the AP store. Its consistency is amazing if used for an apt reason.  It’s a high-quality project – It is well respected in the community. Social networking platform like Facebook built its whole messaging infrastructure on HBase.  Hadoop ecosystem had an operational presence at Salesforce. Experts are applying Hadoop in the product for ages, and they know how it works. HBase uses HDFS for persistence and provides first-class integration with MapReduce. How HBase works? HBase scales in a linear way in order to provide a primary key to each table. Each key space is distributed into sequential block allotted to a region. The RegionServers control each region and distribute the load uniformly in a collected environment. In HBase, you get automating data sharding support, which eliminates the need for manual intervention. After deployment of HBase, HMaster and Zookeeper server are configured to offer cluster topology data to the HBase clients. Client apps linked to these utilities and acquire the list of RegionServers, key ranges, and regions information. It assists the client to determine the accurate data position and connect to the RegionServer directly. Caching support is provided by RegionServers that help in accessing rows frequently. This enhances the performance. Major reasons to use HBase are as under- Even if HBase offers multiple great functionalities, it is still not a ‘Fit for all’ solution. You need to consider following key areas prior using HBase for the application- Data volume – The data volume is one of the common things to consider. You should have PETA data bytes that have to be processed in a distributed environment.  Application type – Hbase is unsuitable for transactional apps, relational analytics, large volume MapReduce jobs, etc. If you have a variable schema with different rows or if you are going for a key dependent access to stored data, you can use HBase. Hardware environment – HBase runs on top of HDFS that works efficiently with a massive amount of nodes. If you are using good hardware, HBase can work for you. No relational features are needed Quick access to data These are the things making HBase so popular among Hadoop and Big data solutions companies. If you are planning to deploy HBase, do consider the above-discussed scenarios for better and efficient performance.

[testrigtechnologies]

What Is Cross-Browser Testing? A Complete Guide for Seamless Web Experiences

In today’s fast-evolving digital landscape, users access websites from a wide array of devices, operating systems, and browsers. From Chrome and Firefox to Safari and Edge—each browser interprets your website code slightly differently. This is where Cross Browser Testing becomes essential.

This blog dives deep into what cross browser testing is, why it matters, what features it covers, and how to do it effectively—ensuring your website delivers a consistent, bug-free experience across all platforms.

What is Cross Browser Testing?

Cross Browser Testing is a type of non-functional testing that verifies whether a web application functions and appears correctly across different web browsers, browser versions, and devices.

It helps developers and QA engineers ensure that:

The UI renders consistently

Core functionalities work correctly

There are no browser-specific bugs or issues

Cross browser testing is not just about aesthetics—it’s about ensuring usability, performance, and accessibility for all users, regardless of how they access your website.

Why is Cross Browser Testing Important?

If you’re only testing your website on Chrome, you’re missing the bigger picture.

Here’s why cross browser testing is crucial:

1. Diverse User Base

Your users might be on Chrome, Safari, Firefox, Edge, or Opera, and using different devices like desktops, tablets, or smartphones. Testing across these ensures everyone has a uniform experience.

2. Browser Rendering Engines Differ

Browsers like Chrome (Blink), Safari (WebKit), and Firefox (Gecko) interpret HTML, CSS, and JavaScript differently. Even a small deviation in rendering can lead to layout breakages or functionality issues.

3. Prevent Loss of Traffic and Conversions

A buggy checkout page on Safari or broken navigation on Firefox can significantly hurt conversion rates and user trust.

4. SEO and Accessibility

Search engines value user experience. Broken layouts or slow load times on certain browsers can negatively affect SEO performance and bounce rates.

What Features are Analyzed in a Cross Browser Test?

Here are the key features and areas evaluated during cross browser testing:

✅ 1. Layout and Design Consistency

CSS rendering

Font sizes, spacing, padding

Media queries and responsiveness

Grid and flex layouts

✅ 2. JavaScript Functionality

Form validation

Dynamic content rendering (DOM updates)

Event handling

Navigation toggles

✅ 3. HTML5 and CSS3 Compatibility

Audio/video elements

Animations

Flexbox, grid, shadows, gradients

✅ 4. Third-Party Integrations

Plugins (chatbots, tracking tools)

Embedded maps or videos

Social sharing buttons

✅ 5. Performance and Speed

Load times across browsers

JavaScript execution speed

Rendering behavior

✅ 6. Security and Cookie Behavior

HTTPS redirection

Local storage and session cookies handling

How is Cross Browser Testing Done?

Cross browser testing can be performed manually or via automation tools. Here's a step-by-step guide:

Step 1: Define Your Browser Coverage

Choose browsers based on:

Your website’s Google Analytics browser report

Global browser usage statistics

Market demographics (e.g., Safari for iOS users)

Example Browser Matrix:

Read also: How Playwright Enhances Cross-Browser Testing Efficiency

Step 2: Set Up Your Test Environment

You can use:

Real Devices: For high accuracy

Emulators/Simulators: Quick tests for layout

Cloud Testing Platforms like:

BrowserStack

Sauce Labs

LambdaTest

CrossBrowserTesting.com

Step 3: Run Tests (Manual or Automated)

🔹 Manual Testing

Test scenarios using real devices and browsers, inspecting UI and performing tasks manually.

🔹 Automated Testing

Use frameworks like:

Selenium

Playwright

Cypress

TestCafe

Automation helps:

Reduce testing time

Run tests in parallel

Integrate with CI/CD pipelines

Step 4: Log and Fix Issues

Document browser-specific bugs, prioritize them, and retest after fixes.

Step 5: Continuous Cross Browser Testing

Use CI tools (Jenkins, GitHub Actions, GitLab CI) to schedule tests automatically on every build or code change.

Best Practices for Cross Browser Testing

✅ Always test on real user data (Google Analytics insights)

✅ Prioritize critical user flows first

✅ Automate repetitive tests, but don’t skip manual exploratory testing

✅ Regularly update browser versions in your testing matrix

✅ Perform regression testing after any major frontend update

Conclusion

Cross Browser Testing is not optional—it’s a necessity in today’s fragmented web ecosystem. Ensuring that your application works flawlessly across all major browsers not only boosts user experience and trust but also strengthens your brand’s credibility

As a leading Web application testing company, at Testrig Technologies, we specialize in comprehensive Cross Browser Testing Services that guarantee flawless digital experiences on any browser, device, or OS. Whether you're launching a new site or scaling an existing one, our QA experts are here to help.

[govindhtech]

AlphaEvolve Coding Agent using LLM Algorithmic Innovation

AlphaEvolve

Large language models drive AlphaEvolve, a powerful coding agent that discovers and optimises difficult algorithms. It solves both complex and simple mathematical and computational issues.

AlphaEvolve combines automated assessors' rigour with LLMs' creativity. This combination lets it validate solutions and impartially assess their quality and correctness. AlphaEvolve uses evolution to refine its best ideas. It coordinates an autonomous pipeline that queries LLMs and calculates to develop algorithms for user-specified goals. An evolutionary method improves automated evaluation metrics scores by building programs.

Human users define the goal, set assessment requirements, and provide an initial solution or code skeleton. The user must provide a way, usually a function, to automatically evaluate produced solutions by mapping them to scalar metrics to be maximised. AlphaEvolve lets users annotate code blocks in a codebase that the system will build. As a skeleton, the remaining code lets you evaluate the developed parts. Though simple, the initial program must be complete.

AlphaEvolve can evolve a search algorithm, the solution, or a function that creates the solution. These methods may help depending on the situation.

AlphaEvolve's key components are:

AlphaEvolve uses cutting-edge LLMs like Gemini 2.0 Flash and Gemini 2.0 Pro. Gemini Pro offers deep and insightful suggestions, while Gemini Flash's efficiency maximises the exploration of many topics. This ensemble technique balances throughput and solution quality. The major job of LLMs is to assess present solutions and recommend improvements. AlphaEvolve's performance is improved with powerful LLMs despite being model-agnostic. LLMs either generate whole code blocks for brief or completely changed code or diff-style code adjustments for focused updates.

Prompt Sample:

This section pulls programs from the Program database to build LLM prompts. Equations, code samples, relevant literature, human-written directions, stochastic formatting, and displayed evaluation results can enhance prompts. Another method is meta-prompt evolution, where the LLM suggests prompts.

Pool of Evaluators

This runs and evaluates proposed programs using user-provided automatic evaluation metrics. These measures assess solution quality objectively. AlphaEvolve may evaluate answers on progressively complicated scenarios in cascades to quickly eliminate less promising examples. It also provides LLM-generated feedback on desirable features that measurements cannot measure. Parallel evaluation speeds up the process. AlphaEvolve optimises multiple metrics. AlphaEvolve can only solve problems with machine-grade solutions, but its automated assessment prevents LLM hallucinations.

The program database stores created solutions and examination results. It uses an evolutionary algorithm inspired by island models and MAP-elites to manage the pool of solutions and choose models for future generations to balance exploration and exploitation.

Distributed Pipeline:

AlphaEvolve is an asynchronous computing pipeline developed in Python using asyncio. This pipeline with a controller, LLM samplers, and assessment nodes is tailored for throughput to produce and evaluate more ideas within a budget.

AlphaEvolve has excelled in several fields:

It improved hardware, data centres, and AI training across Google's computing ecosystem.

AlphaEvolve recovers 0.7% of Google's worldwide computer resources using its Borg cluster management system heuristic. This in-production solution's performance and human-readable code improve interpretability, debuggability, predictability, and deployment.

It suggested recreating a critical arithmetic circuit in Google's Tensor Processing Units (TPUs) in Verilog, removing unnecessary bits, and putting it into a future TPU. AlphaEvolve can aid with hardware design by suggesting improvements to popular hardware languages.

It sped up a fundamental kernel in Gemini's architecture by 23% and reduced training time by 1% by finding better ways to partition massive matrix multiplication operations, increasing AI performance and research. Thus, kernel optimisation engineering time was considerably reduced. This is the first time Gemini optimised its training technique with AlphaEvolve.

AlphaEvolve optimises low-level GPU operations to speed up Transformer FlashAttention kernel implementation by 32.5%. It can optimise compiler Intermediate Representations (IRs), indicating promise for incorporating AlphaEvolve into the compiler workflow or adding these optimisations to current compilers.

AlphaEvolve developed breakthrough gradient-based optimisation processes that led to novel matrix multiplication algorithms in mathematics and algorithm discovery. It enhanced Strassen's 1969 approach by multiplying 4x4 complex-valued matrices with 48 scalar multiplications. AlphaEvolve matched or outperformed best solutions for many matrix multiplication methods.

When applied to over 50 open mathematics problems, AlphaEvolve enhanced best-known solutions in 20% and rediscovered state-of-the-art solutions in 75%. It improved the kissing number problem by finding a configuration that set a new lower bound in 11 dimensions. Additionally, it improved bounds on packing difficulties, Erdős's minimum overlap problem, uncertainty principles, and autocorrelation inequalities. These results were often achieved by AlphaEvolve using problem-specific heuristic search strategies.

AlphaEvolve outperforms FunSearch due to its capacity to evolve across codebases, support for many metrics, and use of frontier LLMs with rich context. It differs from evolutionary programming by automating evolution operator creation via LLMs. It improves artificial intelligence mathematics and science by superoptimizing code.

One limitation of AlphaEvolve is that it requires automated evaluation problems. Manual experimentation is not among its capabilities. LLM evaluation is possible but not the major focus.

AlphaEvolve should improve as LLMs code better. Google is exploring a wider access program and an Early Access Program for academics. AlphaEvolve's broad scope suggests game-changing uses in business, sustainability, medical development, and material research. Future phases include reducing AlphaEvolve's performance to base LLMs and maybe integrating natural-language feedback approaches.

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Troubleshooting Migration Issues: A Practical Guide for Analysts

Migrating business intelligence assets from Tableau to Power BI can be transformative—but it’s rarely without hurdles. For analysts leading or supporting these migrations, troubleshooting technical and functional issues quickly is critical to minimizing disruption and ensuring long-term success. This guide provides practical strategies to help you navigate common pitfalls and troubleshoot migration issues with confidence.

1. Understand the Root of Compatibility Errors

One of the most frequent challenges during a Tableau to Power BI migration is the incompatibility between calculated fields, data types, or visualization logic. Tableau’s flexibility in calculation syntax doesn’t always map cleanly to Power BI’s DAX language.

Tip: Start by auditing all Tableau calculated fields before migration. Use a field-mapping checklist to identify complex calculations that may require DAX rewrites. Tools like Pulse Convert by OfficeSolution can automate parts of this process while highlighting logic mismatches that need manual intervention.

2. Validate Data Model Accuracy

Often, migrated reports look fine at first glance but show discrepancies in totals or aggregation. These are typically caused by differences in how Tableau and Power BI handle relationships and filter contexts.

Tip: Post-migration, validate KPIs and summaries across both platforms using sample data points. Build a test matrix comparing report outputs line by line to quickly isolate calculation or filter logic issues.

3. Resolve Visualization Mismatches

Power BI doesn’t offer one-to-one equivalents for all Tableau visual types or customization features. This can lead to confusion or a loss in report fidelity.

Tip: Prioritize function over form. Identify business-critical dashboards that rely on custom visuals and find the closest Power BI equivalent. Use conditional formatting, bookmarks, and tooltips to replicate interactive features wherever possible.

4. Monitor Performance Bottlenecks

After migration, some dashboards may load slower in Power BI due to less optimized data models or inefficient DAX measures.

Tip: Use Power BI’s built-in Performance Analyzer to pinpoint bottlenecks. Optimize your data model by reducing unnecessary columns, normalizing tables, and leveraging star schema design. Also, review your DAX queries to replace complex logic with more efficient functions.

5. Maintain Stakeholder Confidence

Stakeholders often worry when migrated reports don’t exactly match what they’re used to. Misalignment in visuals, metrics, or navigation can reduce trust in the new platform.

Tip: Maintain a parallel reporting phase where both Tableau and Power BI reports run side-by-side. This ensures accuracy and allows stakeholders to provide feedback, easing the transition.

Conclusion

Troubleshooting during a Tableau to Power BI migration is less about fixing isolated errors and more about proactively managing complexity. By following structured audit, validation, and optimization steps, analysts can deliver smoother transitions and maintain report integrity.

For advanced support and automation in your migration journey, explore Pulse Convert by OfficeSolution at 👉 https://tableautopowerbimigration.com