#copilot interview ai

[interviewassistant]

Upgrade Your Interview Skills with Copilot Interview AI for Success!

Enhance your interview performance with Copilot Interview AI, a cutting-edge tool that offers personalized guidance to tackle common and tough interview questions with confidence. Utilizing advanced technology, this AI-powered assistant interprets questions in real-time and provides tailored responses based on your skills and the job requirements. Whether navigating through technical queries or handling tricky situations, Copilot Interview AI helps craft thoughtful answers and ensures you make a lasting impression on hiring managers. Elevate your interview preparation and increase your chances of landing your dream job by partnering with Copilot Interview AI today!

[lockedinai]

Refine Skills Using AI Interview Copilot Tool

Gain a competitive edge with the AI Interview Copilot Tool, a powerful resource for interview preparation. Powered by LockedIn AI, this tool offers real-time coaching to improve your responses and confidence. Use the AI Interview Copilot Tool to simulate diverse interview formats, receive instant feedback, and refine your answers. Ideal for professionals across industries, it ensures you're fully prepared for technical, behavioral, or panel interviews. Embrace this advanced tool to excel in interviews and secure your dream job. Elevate your preparation with personalized support designed to help you succeed.

[mariacallous]

On Saturday, an Associated Press investigation revealed that OpenAI's Whisper transcription tool creates fabricated text in medical and business settings despite warnings against such use. The AP interviewed more than 12 software engineers, developers, and researchers who found the model regularly invents text that speakers never said, a phenomenon often called a “confabulation” or “hallucination” in the AI field.

Upon its release in 2022, OpenAI claimed that Whisper approached “human level robustness” in audio transcription accuracy. However, a University of Michigan researcher told the AP that Whisper created false text in 80 percent of public meeting transcripts examined. Another developer, unnamed in the AP report, claimed to have found invented content in almost all of his 26,000 test transcriptions.

The fabrications pose particular risks in health care settings. Despite OpenAI’s warnings against using Whisper for “high-risk domains,” over 30,000 medical workers now use Whisper-based tools to transcribe patient visits, according to the AP report. The Mankato Clinic in Minnesota and Children’s Hospital Los Angeles are among 40 health systems using a Whisper-powered AI copilot service from medical tech company Nabla that is fine-tuned on medical terminology.

Nabla acknowledges that Whisper can confabulate, but it also reportedly erases original audio recordings “for data safety reasons.” This could cause additional issues, since doctors cannot verify accuracy against the source material. And deaf patients may be highly impacted by mistaken transcripts since they would have no way to know if medical transcript audio is accurate or not.

The potential problems with Whisper extend beyond health care. Researchers from Cornell University and the University of Virginia studied thousands of audio samples and found Whisper adding nonexistent violent content and racial commentary to neutral speech. They found that 1 percent of samples included “entire hallucinated phrases or sentences which did not exist in any form in the underlying audio” and that 38 percent of those included “explicit harms such as perpetuating violence, making up inaccurate associations, or implying false authority.”

In one case from the study cited by AP, when a speaker described “two other girls and one lady,” Whisper added fictional text specifying that they “were Black.” In another, the audio said, “He, the boy, was going to, I’m not sure exactly, take the umbrella.” Whisper transcribed it to, “He took a big piece of a cross, a teeny, small piece … I’m sure he didn’t have a terror knife so he killed a number of people.”

An OpenAI spokesperson told the AP that the company appreciates the researchers’ findings and that it actively studies how to reduce fabrications and incorporates feedback in updates to the model.

Why Whisper Confabulates

The key to Whisper’s unsuitability in high-risk domains comes from its propensity to sometimes confabulate, or plausibly make up, inaccurate outputs. The AP report says, "Researchers aren’t certain why Whisper and similar tools hallucinate," but that isn't true. We know exactly why Transformer-based AI models like Whisper behave this way.

Whisper is based on technology that is designed to predict the next most likely token (chunk of data) that should appear after a sequence of tokens provided by a user. In the case of ChatGPT, the input tokens come in the form of a text prompt. In the case of Whisper, the input is tokenized audio data.

The transcription output from Whisper is a prediction of what is most likely, not what is most accurate. Accuracy in Transformer-based outputs is typically proportional to the presence of relevant accurate data in the training dataset, but it is never guaranteed. If there is ever a case where there isn't enough contextual information in its neural network for Whisper to make an accurate prediction about how to transcribe a particular segment of audio, the model will fall back on what it “knows” about the relationships between sounds and words it has learned from its training data.

According to OpenAI in 2022, Whisper learned those statistical relationships from “680,000 hours of multilingual and multitask supervised data collected from the web.” But we now know a little more about the source. Given Whisper's well-known tendency to produce certain outputs like "thank you for watching," "like and subscribe," or "drop a comment in the section below" when provided silent or garbled inputs, it's likely that OpenAI trained Whisper on thousands of hours of captioned audio scraped from YouTube videos. (The researchers needed audio paired with existing captions to train the model.)

There's also a phenomenon called “overfitting” in AI models where information (in this case, text found in audio transcriptions) encountered more frequently in the training data is more likely to be reproduced in an output. In cases where Whisper encounters poor-quality audio in medical notes, the AI model will produce what its neural network predicts is the most likely output, even if it is incorrect. And the most likely output for any given YouTube video, since so many people say it, is “thanks for watching.”

In other cases, Whisper seems to draw on the context of the conversation to fill in what should come next, which can lead to problems because its training data could include racist commentary or inaccurate medical information. For example, if many examples of training data featured speakers saying the phrase “crimes by Black criminals,” when Whisper encounters a “crimes by [garbled audio] criminals” audio sample, it will be more likely to fill in the transcription with “Black."

In the original Whisper model card, OpenAI researchers wrote about this very phenomenon: "Because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself."

So in that sense, Whisper "knows" something about the content of what is being said and keeps track of the context of the conversation, which can lead to issues like the one where Whisper identified two women as being Black even though that information was not contained in the original audio. Theoretically, this erroneous scenario could be reduced by using a second AI model trained to pick out areas of confusing audio where the Whisper model is likely to confabulate and flag the transcript in that location, so a human could manually check those instances for accuracy later.

Clearly, OpenAI's advice not to use Whisper in high-risk domains, such as critical medical records, was a good one. But health care companies are constantly driven by a need to decrease costs by using seemingly "good enough" AI tools—as we've seen with Epic Systems using GPT-4 for medical records and UnitedHealth using a flawed AI model for insurance decisions. It's entirely possible that people are already suffering negative outcomes due to AI mistakes, and fixing them will likely involve some sort of regulation and certification of AI tools used in the medical field.

[sequencefairy]

It's very weird to work in an industry that is entirely built from vast sums of people's personal identifiable information and to have the senior leadership in the compliance and privacy spaces both be going all in on the "outsource all your thinking to copilot" train.

Listened to an AVP today in a town hall meeting talk about how cool it was to have an AI crawl her calendar and the calendar of the candidate she was interviewing to find a time that works for both of them to have the interview. The whole time I was sitting there like, do... do we need an AI to do that?

Do we need an AI to book our meetings for us? Are we so fucking inept that we can't ... send a meeting invite and do the little back and forth of rejigging the times/dates if the first one doesn't fit?

In the same town hall, I listened to our VP tell us that she uses copilot every day! She lets it write her emails and her departmental communications and I can extrapolate that to mean that she probably lets it write her performance reviews, too. She wants us all to use it for everything we can, and she's very excited about how much time it's going to save all of us.

(Time saved is such a bullshit thing anyway, 'cause no one is actually saving any time by spending forty minutes crafting a prompt for the stupid thing so it won't lie to you and make shit up and then you have to go through everything it did for you to make sure it's correct and didn't lie to you, and by that point, you might as well have done the thing you asked copilot to do in the first place. And don't get me started on how useless it is at like, actually doing anything helpful - you go ask copilot to help you with anything in excel, I'll wait...)

I also listened to our Financial Crimes guy talk about how sophisticated fraudsters are becoming, and how they're using AI to generate audio and video to fool authentication processes and therefore, get control of people's accounts and take their money.

Like? Guys? Bueller? Anyone?

I really don't think that we should be feeding copilot any client data, ever, even if we say it's a captive version that we have isolated from the web and only given it access to crawl what is within the boundaries we have set for it, as a company. I just feel like this is an unacceptable risk and I am not even CLOSE to the most risk averse person in the compliance space.

It just baffles me that this is even a thing we are DOING, never mind, considering. We are actually doing this. People are using copilot every day. People are probably feeding personal identifiable information into fucking chatgpt 'cause they have no idea why this might be a bad idea, or they just think that because they're accessing it on their workplace-sanctioned device, it's fine?

Maybe, just maybe, we could re-learn how to write emails and book meetings and take notes in a meeting and read legislation and rely on our own brains to do our jobs? Maybe.

Also, like, y'know, it's killing the fucking planet.

[yawpyawp]

the idea of people using chatGPT for legal advice and therapy is SO WILD

chatGPT is literally just if you could ask your iphone’s predictive text function questions in word problem format so it can google things for you

it’s meant to augment a skill you already have, not replace the need for expertise

signed, someone who works with AI professionally

(my main use case for chatGPT and Copilot is to have them basically present me with executive summaries of information pertaining to topics in which i already have some expertise, so i don’t have to do grunt work, but can still tell if it’s hallucinating. it’s a good whisper assist when i’m conducting technical interviews. also, i know prompt engineering, so i know how to mitigate bias in the outputs i get)

[blorbocedes]

I'm doing a little ethnography on new social realities and I'm falling short of interview respondents 🥲

if you or anyone you know talks to AI (ChatGPT or Copilot or Character.AI or Grok or any of them) and talks to it like a friend, like venting and talking about day to day stuff - not doing your work/assignments or using it as Google but like a personal therapist or friend I would loooove to interview you. (Totally anonymous with only as much identifying info as you feel comfortable sharing, done over discord where you just answer some questions as detailed as you want to)

[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

[spaceprincessleia]

So, towards the end of last year, AI has crept more and more into my work (marketing). And to be honest, if I didn't embrace it to a degree, it would be difficult for me to ... exist in my job, or I guess any other job related to my field.

Disclaimer before I continue, I don't work with anything related to AI images, voices, or videos (thankfully). And I've had training/experience in the field I work in done/gained entirely without AI.

I panicked initially, because I had to deal with a lot of "Look at the AI content I created! Please proofread/improve it!" from people whose main writing they do are emails, and various other, similarly annoying instances.

However, I work in a team, luckily, who makes sure that everyone has their place there and room to grow and adapt.

So, I decided to see if I could use this technology without killing the aspects I love about my job, and to be open to other people's use of AI (mainly Chat GPT/Copilot/DeepL).

And you know, I did find things that are easier/faster for me now, for example combining my own notes, memory and impression of a person/conversation with transcripts/summaries of interviews. It doesn't eliminate, but shortens the process, and lets me spend more time doing what I enjoy most - the actual ideation and writing of the final piece.

Same goes for checking shorter texts in English, which isn't my native language, for something as simple as grammar/flow. The result isn't a text that isn't mine anymore. I'll dismiss suggestions (from human editors too, btw), or think about why a different choice of grammar may sound better/more natural.

Most of the time I ask my colleague, and if it were her main job, I always would. But it's not, because we don't have a dedicated role for that, actually (and we're not gonna get one).

What I can't stop either is my colleague using DeepL for her translations. As opposed to me, she has no experience in the translation field, and DeepL doesn't give her that. What I can do, though, is control it, to a degree. By teaching her what a good text should sound like in general, for example, and make her revise the auto translation, or advice her to not use it for the whole text. It's not ideal, but it's the best I can do with the time we have for the task and training.

There's more to be said about those examples, probably, and there are still enough cases of bad/annoying use of AI by people. But I can only control so much.

[bloobluebloo]

Hello!

This might be a weird question, but since you work in IT, do you use AI tools like ChatGPT or Claude a lot, or not at all? I’ve been learning programming for a few months, and honestly, it’s super hard. I’m definitely not a genius, so I use AI a lot to help me figure out what I’m doing and generate code.

The problem is that other students kind of judge and look down on people who use these tools, and it’s making me feel bad about it. Should I stop using AI altogether? I just don’t know how to manage without help or researching all the time.

If you have any tips, they'd really help me out 🙏

Thanks for reading this!

Hey anon! Well, the thing is that the IT industry in its entirety is pushing for AI integration as a whole into their products, so industry-wise it has become sort of inevitable. That being said, because we are still early into the adoption of AI I personally don't use it as I don't have much of a need for it in my current projects. However, Github Copilot is a tool that a lot of my colleagues like to use to assist with their code, and IDEs like IntelliJ have also begun to integrate AI coding assistance into their software. Some of my colleagues do use ChatGPT to ask very obscure and intricate questions about some topics, less to do with getting a direct answer and moreso to get a general idea of what they should be looking at which will segway into my next point. So code generation. The thing is, before the advent of ChatGPT, there already existed plenty of tools that generate boilerplate templates for code. As a software engineer, you don't want to be wasting time reinventing the wheel, so we are already accustomed to using tools to generate code. Where your work actually comes in is writing the logic that is very specific to the way that your project functions. The way I see ChatGPT is that it's a bit smarter than the general libraries and APIs we already use to generate code, but it still doesn't take the entire scope of your project into consideration. The point I am getting at here is that I don't necessarily think there is a problem in generating code, whether you are using AI or anything else, but the problem is do you understand what the code is doing, why it works, and how it will affect your project? Can you take what ChatGPT gives you and actually optimize it to the specifics of your project, or do you just inject it, see that it works, and go on your merry way without another thought as to why it worked? So, I would say, as a student, I would suggest trying not to use ChatGPT to generate code, because it defeats the purpose of learning code. Software engineering as a whole is tough! It is actually the nature of the beast that, at times, you will spend hours trying to solve a specific problem, and often times the solution at the end is to add one line in one very specific place which can feel anticlimactic after so much effort. However, what you get from all those hours of debugging, researching, and asking questions is a wealth of knowledge that you can add to your toolbox, and that is what is most important as a software developer. The IT landscape is rapidly changing; you might be expected to pick up a different programming language and different framework within weeks, you might suddenly be saddled with a new project you've never seen in your life, or you might suddenly have something new like AI thrown at you where you suddenly have to take it into consideration in your current work. You can only keep up with this sort of environment if you have a good understanding of programming fundamentals. So, try not to lean too much on things like ChatGPT because it will get you through today, but it will hurt you down the line (like in tech interviews, for example).

[interviewassistant]

Introducing Interview Copilot by Interview Assistant AI

Elevate your interview preparation with Interview Copilot, a real-time assistant powered by AI available at Interview Assistant AI. Used by 4000 jobseekers, this revolutionary tool ensures you ace your interviews by providing unparalleled support. Say goodbye to subscription fees with a one-time payment model. Whether you prefer the web-based interface or the powerful desktop application, Interview Copilot is your go-to solution. With advanced features like parallel translation in multiple languages and live coding simulations, Interview Copilot is your ultimate wingman for securing your dream job. Try for free now and experience the difference!

[mariacallous]

One phrase encapsulates the methodology of nonfiction master Robert Caro: Turn Every Page. The phrase is so associated with Caro that it’s the name of the recent documentary about him and of an exhibit of his archives at the New York Historical Society. To Caro it is imperative to put eyes on every line of every document relating to his subject, no matter how mind-numbing or inconvenient. He has learned that something that seems trivial can unlock a whole new understanding of an event, provide a path to an unknown source, or unravel a mystery of who was responsible for a crisis or an accomplishment. Over his career he has pored over literally millions of pages of documents: reports, transcripts, articles, legal briefs, letters (45 million in the LBJ Presidential Library alone!). Some seemed deadly dull, repetitive, or irrelevant. No matter—he’d plow through, paying full attention. Caro’s relentless page-turning has made his work iconic.

In the age of AI, however, there’s a new motto: There’s no need to turn pages at all! Not even the transcripts of your interviews. Oh, and you don’t have to pay attention at meetings, or even attend them. Nor do you need to read your mail or your colleagues’ memos. Just feed the raw material into a large language model and in an instant you’ll have a summary to scan. With OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude as our wingmen, summary reading is what now qualifies as preparedness.

LLMs love to summarize, or at least that’s what their creators set them about doing. Google now “auto-summarizes” your documents so you can “quickly parse the information that matters and prioritize where to focus.” AI will even summarize unread conversations in Google Chat! With Microsoft Copilot, if you so much as hover your cursor over an Excel spreadsheet, PDF, Word doc, or PowerPoint presentation, you’ll get it boiled down. That’s right—even the condensed bullet points of a slide deck can be cut down to the … more essential stuff? Meta also now summarizes the comments on popular posts. Zoom summarizes meetings and churns out a cheat sheet in real time. Transcription services like Otter now put summaries front and center, and the transcription itself in another tab.

Why the orgy of summarizing? At a time when we’re only beginning to figure out how to get value from LLMs, summaries are one of the most straightforward and immediately useful features available. Of course, they can contain errors or miss important points. Noted. The more serious risk is that relying too much on summaries will make us dumber.

Summaries, after all, are sketchy maps and not the territory itself. I’m reminded of the Woody Allen joke where he zipped through War and Peace in 20 minutes and concluded, “It’s about Russia.” I’m not saying that AI summaries are that vague. In fact, the reason they’re dangerous is that they’re good enough. They allow you to fake it, to proceed with some understanding of the subject. Just not a deep one.

As an example, let’s take AI-generated summaries of voice recordings, like what Otter does. As a journalist, I know that you lose something when you don’t do your own transcriptions. It’s incredibly time-consuming. But in the process you really know what your subject is saying, and not saying. You almost always find something you missed. A very close reading of a transcript might allow you to recover some of that. Having everything summarized, though, tempts you to look at only the passages of immediate interest—at the expense of unearthing treasures buried in the text.

Successful leaders have known all along the danger of such shortcuts. That’s why Jeff Bezos, when he was CEO of Amazon, banned PowerPoint from his meetings. He famously demanded that his underlings produce a meticulous memo that came to be known as a “6-pager.” Writing the 6-pager forced managers to think hard about what they were proposing, with every word critical to executing, or dooming, their pitch. The first part of a Bezos meeting is conducted in silence as everyone turns all 6 pages of the document. No summarizing allowed!

To be fair, I can entertain a counterargument to my discomfort with summaries. With no effort whatsoever, an LLM does read every page. So if you want to go beyond the summary, and you give it the proper prompts, an LLM can quickly locate the most obscure facts. Maybe one day these models will be sufficiently skilled to actually identify and surface those gems, customized to what you’re looking for. If that happens, though, we’d be even more reliant on them, and our own abilities might atrophy.

Long-term, summary mania might lead to an erosion of writing itself. If you know that no one will be reading the actual text of your emails, your documents, or your reports, why bother to take the time to dig up details that make compelling reading, or craft the prose to show your wit? You may as well outsource your writing to AI, which doesn’t mind at all if you ask it to churn out 100-page reports. No one will complain, because they’ll be using their own AI to condense the report to a bunch of bullet points. If all that happens, the collective work product of a civilization will have the quality of a third-generation Xerox.

As for Robert Caro, he’s years past his deadline on the fifth volume of his epic LBJ saga. If LLMs had been around when he began telling the president’s story almost 50 years ago—and he had actually used them and not turned so many pages—the whole cycle probably would have been long completed. But not nearly as great.

[lgaretio]

La nueva realidad de la programación (y de la educación): inteligencia artificial, de trampa a requisito » Enrique Dans

Una noticia publicada ayer en The Register, «Canva to job candidates: Thou shalt use AI during interviews«, revela cómo la multinacional australiana Canva ha incorporado el uso obligatorio de asistentes de programación basados en inteligencia artificial como GitHub Copilot, Cursor, Claude o...

[gvtacademy]

Inside the Course: What You'll Learn in GVT Academy's Data Analyst Program with AI and VBA

If you're searching for the Best Data Analyst Course with VBA using AI in Noida, GVT Academy offers a cutting-edge curriculum designed to equip you with the skills employers want in 2025. In an age where data is king, the ability to analyze, automate, and visualize information is what separates good analysts from great ones.

Let’s explore the modules inside this powerful course — from basic tools to advanced technologies — all designed with real-world outcomes in mind.

Module 1: Advanced Excel – Master the Basics, Sharpen the Edge

You start with Advanced Excel, a must-have tool for every data analyst. This module helps you upgrade your skills from intermediate to advanced level with:

Advanced formulas like XLOOKUP, IFERROR, and nested functions

Data cleaning techniques using Power Query

Creating interactive dashboards with Pivot Tables

Case-based learning from real business scenarios

This strong foundation ensures you're ready to dive deeper into automation and analytics.

Module 2: VBA Programming – Automate Your Data Workflow

Visual Basic for Applications (VBA) is a game-changer when it comes to saving time. Here’s what you’ll learn:

Automate tasks with macros and loops

Build interactive forms for better data entry

Develop automated reporting tools

Integrate Excel with external databases or emails

This module gives you a serious edge by teaching real-time automation for daily tasks, making you stand out in interviews and on the job.

Module 3: Artificial Intelligence for Analysts – Data Meets Intelligence

This is where things get futuristic. You’ll learn how AI is transforming data analysis:

Basics of machine learning with simple use cases

Use AI tools (like ChatGPT or Excel Copilot) to write smarter formulas

Forecast sales or trends using Python-based models

Explore AI in data cleaning, classification, and clustering

GVT Academy blends the power of AI and VBA to offer a standout Data Analyst Course in Noida, designed to help students gain a competitive edge in the job market.

Module 4: SQL – Speak the Language of Databases

Data lives in databases, and SQL helps you retrieve it efficiently. This module focuses on:

Writing SELECT, JOIN, and GROUP BY queries

Creating views, functions, and subqueries

Connecting SQL output directly to Excel and Power BI

Handling large volumes of structured data

You’ll practice on real datasets and become fluent in working with enterprise-level databases.

Module 5: Power BI – Turn Data into Stories

More than numbers, data analysis is about discovering what the numbers truly mean. In the Power BI module, you'll:

Import, clean, and model data

Create interactive dashboards for business reporting

Use DAX functions to create calculated metrics

Publish and share reports using Power BI Service

By mastering Power BI, you'll learn to tell data-driven stories that influence business decisions.

Module 6: Python – The Language of Modern Analytics

Python is one of the most in-demand skills for data analysts, and this module helps you get hands-on:

Python fundamentals: Variables, loops, and functions

Working with Pandas, NumPy, and Matplotlib

Data manipulation, cleaning, and visualization

Introduction to machine learning with Scikit-Learn

Even if you have no coding background, GVT Academy ensures you learn Python in a beginner-friendly and project-based manner.

Course Highlights That Make GVT Academy #1

👨‍🏫 Expert mentors with industry experience

🧪 Real-life projects for each module

💻 Live + recorded classes for flexible learning

💼 Placement support and job preparation sessions

📜 Certification recognized by top recruiters

Every module is designed with job-readiness in mind, not just theory.

Who Should Join This Course?

This course is perfect for:

Freshers wanting a high-paying career in analytics

Working professionals in finance, marketing, or operations

B.Com, BBA, and MBA graduates looking to upskill

Anyone looking to switch to data-driven roles

Final Words

If you're looking to future-proof your career, this course is your launchpad. With six powerful modules and job-focused training, GVT Academy is proud to offer the Best Data Analyst Course with VBA using AI in Noida — practical, placement-driven, and perfect for 2025.

📞 Don’t Miss Out – Limited Seats. Enroll Now with GVT Academy and Transform Your Career!

1. Google My Business: http://g.co/kgs/v3LrzxE

2. Website: https://gvtacademy.com

3. LinkedIn: www.linkedin.com/in/gvt-academy-48b916164

4. Facebook: https://www.facebook.com/gvtacademy

5. Instagram: https://www.instagram.com/gvtacademy/

6. X: https://x.com/GVTAcademy

7. Pinterest: https://in.pinterest.com/gvtacademy

8. Medium: https://medium.com/@gvtacademy

[bloggersmith]

Unlocking the Power of Generative AI & Prompt Engineering for QA Engineers

In today's fast-evolving software landscape, Quality Assurance (QA) is no longer confined to traditional manual testing methods. The integration of Generative AI and Prompt Engineering is revolutionizing how QA engineers design, execute, and manage testing processes. Magnitia’s course on Generative AI & Prompt Engineering for QA Engineers is tailored to empower professionals with the knowledge and skills to stay ahead in this AI-driven era.

Why QA Needs Generative AI

Generative AI, such as ChatGPT and similar LLMs (Large Language Models), can produce human-like text and logic-based outputs. For QA professionals, this means:

Automated test case generation based on user stories or requirements

Bug report summarization and prioritization

Smart script generation for automation frameworks like Selenium or Playwright

Instant documentation creation for better traceability

When applied properly, Generative AI can drastically reduce time-to-test while increasing coverage and accuracy.

What is Prompt Engineering?

Prompt engineering is the process of crafting precise and efficient prompts to communicate with AI models. For QA engineers, mastering this skill means:

Generating highly accurate test cases from vague inputs

Extracting specific validation scenarios from documentation

Building reusable QA templates that AI can use consistently

Validating functional and edge cases through AI simulations

Course Overview at Magnitia

The Generative AI & Prompt Engineering for QA Engineers course offers a hands-on, project-based approach. Here’s what learners can expect:

Key Modules:

Introduction to Generative AI in QA

Fundamentals of Prompt Engineering

Crafting Test Cases using AI

Automating Regression and Unit Testing with AI Tools

Writing Functional Test Scenarios from Business Requirements

Enhancing Defect Analysis and Reporting with AI

Integration with Testing Frameworks and CI/CD Pipelines

Real-time Project Simulations and Case Studies

Tools Covered:

OpenAI (ChatGPT)

GitHub Copilot

Test Automation tools (Playwright, Selenium)

AI-based documentation tools

API testing with Postman & AI plugins

Who Should Enroll?

This course is ideal for:

Manual testers looking to transition into automation

Automation testers wanting to enhance productivity

QA leads and managers aiming to optimize testing processes

Anyone interested in AI’s role in Quality Engineering

Benefits of the Course

Industry-relevant curriculum aligned with real-world applications

Expert-led sessions with insights from AI and QA veterans

Hands-on projects to build practical experience

Certification to validate your AI & QA integration skills

Career support including mock interviews and resume guidance

Future-Proof Your QA Career

As AI continues to reshape the technology landscape, QA engineers must adapt and evolve. By mastering generative AI and prompt engineering, QA professionals not only increase their value but also contribute to faster, smarter, and more resilient testing strategies.

Enroll now at Magnitia to harness the full potential of Generative AI in QA and become a leader in the next generation of software testing.

[uwteam]

13 czerwca 2025

◢ #unknownews ◣

Zapraszam do lektury dzisiejszego wydania newslettera.

1) Mało uczciwe taktyki producentów drukarek — jak robią nas w konia? (film, 20m) https://www.youtube.com/watch?v=jrTekOIVZco INFO: Drukarki sprzedawane za grosze, a tusz droższy niż ludzka krew? Tak to niestety obecnie wygląda. Film dość szczegółowo pokazuje, jak działają korporacyjne strategie sprzedażowe, które mają zwiększyć zyski koncernów produkujących drukarki, oczywiście wszystko kosztem użytkowników tych urządzeń. Jak walczy się z zamiennikami do tuszu? Jak utrudnia się samodzielne napełnianie kartridży albo jak wymusza się na użytkownikach częstsze kupowanie atramentu, którego nawet nie potrzebują? Tego wszystkiego dowiesz się z filmu.

2) Jak zaplanować prace utrzymaniowe w zespole inżynierów? https://hybridhacker.email/p/how-to-plan-for-maintenance INFO: Zarządzanie zadaniami utrzymaniowymi to jeden z większych problemów zespołów inżynierskich — są one zwykle techniczne, niepilne, a zarazem super potrzebne (zapobiegają kumulowaniu się problemów na później). Z artykułu dowiesz się, czym jest reguła "Boy Scout" oraz na czym polegają dyżury "bug duty". Z artykułu dowiesz się, jak takie prace utrzymaniowe ogarnąć w swoim zespole.

3) A co jeśli kandydat skorzysta z AI na rozmowie o pracę? — powinien skorzystać! https://www.canva.dev/blog/engineering/yes-you-can-use-ai-in-our-interviews/ INFO: Canva zmienia swoje podejście do rozmów technicznych i teraz nie tylko zezwala, ale wręcz oczekuje od kandydatów korzystania z narzędzi AI, takich jak Copilot, Cursor czy Claude. Firma dostosowała zadania rekrutacyjne, aby odzwierciedlały realne wyzwania, z jakimi programiści spotykają się w pracy. Mniej algorytmów, więcej pracy z kodem generowanym przez AI, analizą wymagań i podejmowaniem decyzji. Celem tej zmiany jest ocena rzeczywistych umiejętności współpracy z AI, które kandydaci będą wykorzystywać na co dzień po dołączeniu do zespołu. Dość nietypowe podejście.

4) Jak zminimalizowałem prokrastynację dzięki... drukarce paragonów (i nie tylko) https://www.laurieherault.com/articles/a-thermal-receipt-printer-cured-my-procrastination INFO: Autor opisuje swoją długoletnią walkę z odkładaniem rzeczy na później i przedstawia rozwiązanie, które pomogło mu rozwiązać ten problem. Wykorzystał mechanikę znaną z gier komputerowych oraz drukarkę paragonów (ale prawdopodobnie równie dobrze sprawdziłyby się samoprzylepne karteczki). Autor proponuje używanie wspomnianej drukarki termicznej jako "wypluwarki zadań", co zwiększa poczucie realizmu tego, co robimy i według niego silniej motywuje do działania.

5) Czy AI umie liczyć kalorie? — test kilku aplikacji https://lifehacker.com/health/ai-powered-calorie-counting-apps-worse-than-expected INFO: Na rynku pojawiło się mnóstwo aplikacji, które na podstawie fotki tego, co masz na talerzu, powiedzą Ci, ile to mniej więcej ma kalorii. Tak przynajmniej to powinno działać według twórców tych aplikacji. A jak jest naprawdę? Autor przetestował kilka popularnych narzędzi (Cal AI, SnapCalorie, Calorie Mama) i opisał, jakie są z nimi problemy. Nie wygląda to obecnie zbyt optymistycznie.

6) Jak zbudować budżetowy serwer pod LLMy za $1300 https://www.informationga.in/blog/building-an-ai-server-on-a-budget INFO: Autor pokazuje krok po kroku, jak zbudował od zera własny serwer do zastosowań AI, mieszcząc się w niewielkim budżecie. Jaki GPU można wybrać przy tak niewielkim budżecie? Ile RAM-u realnie potrzebujemy? Co z zasilaniem tego wszystkiego? Autor wyjaśnia także, dlaczego warto posiadać własny sprzęt do eksperymentów z AI, a nie zdecydować się na chmurę.

7) Czym jest localhost tracking i jak dzięki temu Meta może Cię śledzić? https://www.zeropartydata.es/p/localhost-tracking-explained-it-could?hide_intro_popup=true INFO: Firma Meta opracowała mechanizm nazwany "localhost tracking", który pozwala aplikacjom Facebooka i Instagrama nasłuchiwać danych z przeglądarki mobilnej — nawet w trybie incognito, przy włączonym VPN-ie i bez zgody na ciasteczka. Jak to działa, tego dowiesz się z artykułu. Sprytne.

8) Co wydarzyło się w świecie LLM-ów w ostatnich sześciu miesiącach? https://simonwillison.net/2025/Jun/6/six-months-in-llms/ INFO: Ogromne tempo rozwoju modeli językowych LLM sprawiło, że tylko w ostatnim półroczu pojawiło się ponad 30 nowych modeli (z puli tych istotnych dla branży AI), w tym Llama 3.3, DeepSeek R1, Claude 3.7 czy Gemini 2.5 Pro. Autor testował je w dość nietypowy sposób, prosząc o wygenerowanie pelikana na rowerze w formacie SVG, co pozwalało nie tylko sprawdzić zdolność do generowania kodu, ale też poziom rozumienia złożonych zadań. W artykule znajdziesz ciekawe przemyślenia na temat skuteczności modeli lokalnych, absurdalnych różnicach cenowych (porównaj np. Nova Micro z o1 Pro).

9) Jak działały karty telefoniczne z lat 90. i jakie sekrety skrywały (film, 6m) https://www.youtube.com/watch?v=1Bb6iPxm2sU INFO: Film przedstawia historię kart telefonicznych stosowanych w polskich budkach od lat 60. aż po lata 2000. Można się z niego dowiedzieć, jak działały magnetyczne i chipowe karty, ile kosztował impuls i jak całe systemy telefoniczne były ówcześnie wykorzystywane (czasem również niezgodnie z prawem). Sporo nostalgii, ale też garść technicznych informacji z epoki przed smartfonami.

10) Weryfikacja wieku w internecie według UE — na czym ma polegać i czy to ma sens? (film, 15m) https://www.youtube.com/watch?v=Tc5YXj6BlcE INFO: Unia Europejska zapowiada wprowadzenie Mini ID Wallet, czyli systemu cyfrowej weryfikacji wieku przed dostępem do wybranych stron i usług online. Film omawia, jak to rozwiązanie ma działać, gdzie będzie obowiązywać i czym różni się od pełnego europejskiego portfela tożsamości, nad którym UE także pracuje. Wspomniany jest tutaj temat prywatności, możliwego wykluczenia cyfrowego oraz technologii Zero Knowledge Proof, która ma stać za tym systemem.

11) Jak sprawić, aby Twoje strony dobrze wyglądały na papierze — stylowanie do druku https://piccalil.li/blog/printing-the-web-making-webpages-look-good-on-paper/ INFO: Drukowanie stron internetowych nie jest tylko reliktem przeszłości. Wiele osób nadal potrzebuje wersji papierowych, np. ze względów dostępności czy chęci wzięcia danych w podróż, gdzie nie będzie dostępu do internetu (np. bilet, mapa, jakiś plan konferencji itp.). Artykuł krok po kroku pokazuje, jak wprowadzić style CSS ograniczone do kontekstu wydruku. Autor omawia też sposoby testowania takiego stylowania oraz wyjaśnia, jak kontrolować sensowny podział dużych dokumentów na strony przy wydruku.

12) DeskHog – kieszonkowa konsolka developerska z ESP32 i otwartym kodem https://posthog.com/deskhog INFO: DeskHog to niewielkie urządzenie (z serii "zrób to sam") bazujące na ESP32-S3 z kolorowym ekranem TFT, Wi-Fi i baterią wystarczającą na około 10 godzin działania. Może służyć jako mikro konsola do gier, terminal do wyświetlania danych lub jako przenośny gadżet developerski.

13) Jak używamy Claude do pisania prawdziwego kodu produkcyjnego? https://diwank.space/field-notes-from-shipping-real-code-with-claude INFO: Autor dzieli się tym, jak podczas pracy w poważnych aplikacjach produkcyjnych korzysta z narzędzi AI. Opisuje, jakimi sztuczkami się posługuje, jak wykorzystuje na przykład plik CLAUDE.md. Przedstawia swoje strategie commitowania oraz wyjaśnia, dlaczego pisze testy samodzielnie. Tłumaczy także, jakie są jego trzy tryby korzystania z tego narzędzia. Przy okazji podkreśla, jak kontekst, dokumentacja i komentarze w kodzie mogą zmienić sztuczną inteligencję w naprawdę efektywnego pomocnika.

14) Boty z Wietnamu i masowa manipulacja w mediach społecznościowych? (film, 18m) https://www.youtube.com/watch?v=A2scWe8sFPI INFO: Wietnam stał się cyfrowym centrum manipulacji online, gdzie wiele firm oferuje swoje usługi polegające na generowaniu fałszywych komentarzy i fałszywych lajków, ale firmy te przyjmują także zlecenia związane z manipulacją polityczną, także tą wycelowaną w Europę. Film pokazuje skalę tego problemu. Jeśli masz ochotę na "film z żółtymi napisami", to ten będzie idealny ;)

15) Jak zbudować własny framework CSS krok po kroku https://scientyficworld.org/building-my-own-css-framework/ INFO: Autor opisuje proces tworzenia NimbleCSS, czyli lekkiego, hybrydowego frameworka CSS dostosowanego do jego indywidualnego stylu pracy. Artykuł omawia kluczowe decyzje architektoniczne: wybór podejścia (utility-first, component-based, classless), strukturę plików projektu, automatyzację kompilacji, publikację całości jako paczki npm oraz wykorzystanie PostCSS. Ciekawy poradnik, użyteczny zwłaszcza dla zespołów programistycznych, które chciałyby stworzyć rozwiązanie idealnie dostosowane do realiów ich pracy.

16) Jak działa jazda na berło i dlaczego stosuje się ją w Poznaniu? (film, 9m) https://www.youtube.com/watch?v=j9mtIxJM24A INFO: Coś dla fanów kolejnictwa, ale wrzucam, bo technicznie jest to dość ciekawym rozwiązaniem. Koncepcja jazdy na berło to jeden z najstarszych sposobów zabezpieczenia ruchu kolejowego na jednotorowych odcinkach linii. Film wyjaśnia, czym dokładnie jest to "berło", jak działa cały system i dlaczego mimo rozwoju technologii wciąż bywa stosowany (np. w Poznaniu). W materiale znajdziesz także omówienie różnych wersji systemu (od drewnianych pałek po nowoczesne, cyfrowe tokeny), sposoby ich przekazywania między maszynistami oraz ciekawostki z historii kolei. Sprytne rozwiązanie i ciekawy film.

17) Somo – czytelna i interaktywna alternatywa dla netstata (Linux) https://github.com/theopfr/somo INFO: Aplikacja pokazuje aktywne połączenia sieciowe i nasłuchujące porty w czytelnej tabelce, z możliwością filtrowania po protokole, porcie, IP czy nazwie procesu. Pozwala też interaktywnie zabić podejrzane procesy korzystające z danego połączenia.

18) be-a11y – narzędzie CLI do wykrywania problemów z dostępnością w HTML https://github.com/be-lenka/be-a11y INFO: Ta aplikacja skanuje pliki HTML w Twoim repozytorium (ale potrafi też przeskanować zdalny URL) i raportuje wszelkie niezgodności związane z dostępnością. Weryfikowane jest przestrzeganie standardu WCAG 2.1. Dowiesz się, czy używane przez Ciebie kolory są odpowiednio kontrastowe, czy wszystkie ALT-y przy grafikach są wypełnione, nagłówki mają odpowiednią kolejność, przyciski naprawdę są przyciskami, a linki linkami (czasami frontendowcy kombinują ze stylami). Dzięki temu, że jest to narzędzie CLI, możesz je dość łatwo zintegrować z systemami CI/CD.

19) Różnice między 1fr 1fr, auto auto i 50% 50% w CSS Grid https://frontendmasters.com/blog/1fr-1fr-vs-auto-auto-vs-50-50/ INFO: Te trzy wymienione w tytule sposoby na definiowanie dwóch kolumn w CSS wydają się, że działają tak samo, albo przynajmniej podobnie, ale w rzeczywistości mocno się od siebie różnią. Artykuł tłumaczy, jak zastosowanie tych różnych wartości zmienia sposób reakcji na przykład na obecność odstępu (gap), zawartość o różnej szerokości albo osadzone w kolumnach obrazki. Jeśli metody te działają niezwykle podobnie do siebie, to która z nich jest tą najlepszą?

20) Pilot z PRL-a, który działał na... gwizdnięcie (film, 18m) https://www.youtube.com/watch?v=HsdIY2UOzGM INFO: Zaskakujące rozwiązanie technologiczne z czasów PRL. Zdalne sterowanie urządzeniami (prawie smart home! :D) oparte na gumowej gruszce z gwizdkiem. Z filmu dowiesz się, jakim cudem to działało, ile kosztowało i do czego tak naprawdę służyło. Taki powiew nostalgii z dawnych lat. Coś dla pasjonatów elektroniki.

21) GitLab skrócił czas wykonywania backupu z 48h do 41 min - jak to osiągnęli? https://about.gitlab.com/blog/2025/06/05/how-we-decreased-gitlab-repo-backup-times-from-48-hours-to-41-minutes/ INFO: Firma zidentyfikowała poważny problem wydajnościowy w funkcji narzędzia GIT odpowiedzialnej za tworzenie paczek bundle. Poziom złożoności tej funkcji sprawiał, że backup największych repozytoriów na platformie mógł trwać nawet 48 godzin, ale po poprawkach czas ten skrócił się z godzin do minut. Istnieje szansa, że wypracowane rozwiązanie przyspieszające tworzenie paczek zostanie wdrożone globalnie do klienta GIT.

22) Fałszywe założenia programistów na temat lotnictwa https://flightaware.engineering/falsehoods-programmers-believe-about-aviation/ INFO: Zbiór mitów i błędnych przekonań, które mogą prowadzić do poważnych problemów przy projektowaniu systemów przetwarzających dane lotnicze. Od niejednoznaczności identyfikatorów lotów, przez zaskakujące reguły kodowania lotnisk, po dziwactwa związane z transponderami i sygnałami ADS-B. To wszystko pokazuje, jak złożony i nieprzewidywalny potrafi być świat danych w lotnictwie. Jeśli pracujesz lub masz zamiar pracować z danymi lotniczymi, to warto rzucić okiem.

23) Dlaczego Railway rezygnuje z Nix na rzecz Railpack? https://blog.railway.com/p/introducing-railpack INFO: Wydaje się, że wszyscy w branży IT zaczynają chwalić Nixa, a tymczasem Railway porzuca go na rzecz nowego buildera o nazwie Railpack, który według nich poprawia precyzję wersjonowania, zmniejsza rozmiary obrazów nawet o 77%, a do tego oferuje lepszą obsługę cache dzięki integracji z BuildKit.

24) Analiza luki w kliencie pocztowym Roundcube https://fearsoff.org/research/roundcube INFO: Artykuł opisuje podatność odkrytą w popularnym, otwartoźródłowym kliencie webmailowym Roundcube. Omawia sposób działania ataku, warunki jego wykorzystania oraz potencjalne konsekwencje dla użytkowników. Znajdziesz tam także zalecenia dotyczące zabezpieczeń i wskazówki dla administratorów systemów korzystających z Roundcube.

25) Browser Use – otwartoźródłowi agenci AI do automatyzacji przeglądarki https://github.com/browser-use/browser-use INFO: To biblioteka (i platforma chmurowa zarazem) pozwalająca agentom AI samodzielnie obsługiwać przeglądarkę internetową. Mogą otwierać strony, czytać dane, wypełniać formularze, wykonywać akcje. Całość działa na Playwright i współpracuje z popularnymi modelami językowymi (tymi od OpenAI, Anthropic, DeepSeek itd.), a do uruchomienia wymaga jedynie kilku linii kodu w Pythonie. Projekt wspiera też automatyzację z poziomu Web UI, desktopowej aplikacji oraz interaktywnego CLI. Wersja self-hosted jest darmowa. Płacić trzeba jedynie za wersję chmurową.

26) Podatność IDOR w Morele.net umożliwiała dostęp do danych klientów https://zaufanatrzeciastrona.pl/post/spora-wpadka-morele-net-mozna-bylo-pobierac-dane-wszystkich-klientow/ INFO: W formularzu ratalnym na wspomnianym serwisie wykryto podatność typu IDOR, która pozwalała na odczytywanie danych (adres e-mail, numer telefonu, kwota zamówienia) dowolnych klientów, jeśli znało się ich numer zamówienia i było zalogowanym użytkownikiem (zalogowanym na dowolne konto). Luka została zgłoszona przez czytelnika i załatana w ciągu 2 godzin, a według serwisu nie została szerzej wykorzystana.

27) Jak SEO psuje internet i jak się przed tym bronić? https://www.internet-czas-dzialac.pl/seo-belkot/ INFO: Ciekawa analiza zjawiska tzw. SEO Bełkotu, czyli tworzenia stron internetowych przeładowanych frazami kluczowymi i sztucznie rozwleczonymi treściami, które utrudniają użytkownikowi szybkie dotarcie do potrzebnych informacji, ale za to całkiem nieźle pozycjonują się w wyszukiwarkach. Problemem nie jest bezpośrednio SEO, a coś odrobinę innego. O co chodzi i jak z tym zjawiskiem walczyć, tego dowiesz się z artykułu.

28) Reverse engineering klienta LLM w Cursorze z użyciem TensorZero https://www.tensorzero.com/blog/reverse-engineering-cursors-llm-client/ INFO: Autorzy stworzyli coś na wzór proxy między środowiskiem Cursor a dostawcami LLM, wykorzystując framework TensorZero. Przechwytywali i analizowali każde zapytanie oraz odpowiedź modelu. Pokazują, jak za pomocą proxy i poprawnej konfiguracji (m.in. CORS i auth) można uzyskać pełną przejrzystość działania tej aplikacji AI i eksperymentować z modelami, promptami oraz danymi wyjściowymi. Ostatecznie udało się zobaczyć, jakie konkretne dane (w tym prompty i stan użytkownika) Cursor wysyła do modeli językowych. Ciekawostka dla bardziej zaawansowanych użytkowników.

29) Architektura systemu WhatsAppa i sposoby radzenia sobie z ogromną skalą (film, 14m) https://www.youtube.com/watch?v=fFPV8zO_S40 INFO: Film wyjaśnia, jak zbudowana jest infrastruktura WhatsAppa. Od połączeń TCP i wykorzystania Erlanga, przez "chat serwery" rozproszone geograficznie, po mechanizmy zapewniające wysoką odporność na błędy. Z filmu dowiesz się, jak wygląda routing wiadomości w czasie rzeczywistym, przechowywanie tymczasowych danych, przełączanie między trybami online/offline, komunikacja w grupach i jak ogarnięto tryb multidevice. Sporo technologicznych ciekawostek.

== LINKI TYLKO DLA PATRONÓW ==

30) Budowanie i sprzedaż agentów AI bez kodu z użyciem n8n (film, 8h i 30m) https://uw7.org/un_b68086aa47302 INFO: Rozbudowany, ponad 8-godzinny kurs tworzenia agentów AI bez potrzeby pisania kodu, z użyciem platformy n8n. Film omawia nie tylko podstawy automatyzacji i działania agentów AI, ale także przeprowadza przez proces budowania ponad 15 rzeczywistych automatyzacji. Od prostych scenariuszy po systemy z pamięcią, architekturą wielu agentów, własnymi API itp. Dużo praktycznych przykładów, całość zrealizowana przyjaznym językiem, z omówieniem integracji, credentiali i technik debugowania. Idealne dla osób z IT chcących wejść w temat AI/Automation jako dostawcy rozwiąza�� lub twórcy usług produktowych.

31) "Tańczący z botami" - nagranie mojego wystąpienia z Infoshare 2025 https://patronite.pl/post/82748/tanczacy-z-botami-nagranie-z-infoshare-2025 INFO: Na tegorocznej edycji Infoshare dzieliłem się inspiracjami na automatyzacje w życiu prywatnym i zawodowym. Nie jest to typowy tutorial, a raczej pokazanie możliwości, co da się zautomatyzować, aby żyć spokojniej i mniej martwić się np. o swój biznes, będąc na urlopie. Uprzedzając pytania: tak, byłem mocno przeziębiony podczas wygłaszania prelekcji ;)

[techit-rp]

The Rise of AI-Powered Financial Modeling: What It Means for Future Investment Bankers

Financial modeling has long been the backbone of investment banking—driving valuations, mergers, acquisitions, and strategic decisions. But in 2025, this skill is undergoing a radical transformation. AI-powered financial modeling is no longer just a futuristic concept—it’s here, and it’s reshaping how analysts work, how banks operate, and how careers in finance are built.

If you're planning to learn investment banking in Chennai, understanding this evolution is crucial to staying relevant and future-ready in an industry being redefined by technology.

What is AI-Powered Financial Modeling?

AI-powered financial modeling refers to the integration of artificial intelligence tools into the traditional processes of building financial models. These tools use machine learning and natural language processing to assist in tasks that used to take hours or days—like populating income statements, forecasting cash flows, conducting scenario analysis, or building discounted cash flow models.

Imagine a world where, instead of manually entering data line-by-line into Excel, an AI tool scans earnings reports, pulls the relevant figures, and generates a first draft of your model. That’s the future of finance, and it's already starting to become the present.

How AI is Transforming the Role of a Financial Analyst

Today’s junior investment banker is no longer just a spreadsheet wizard. With the help of AI, analysts can automate repetitive tasks like formatting pitchbooks, inputting historical data, and running sensitivity analysis. This shift allows professionals to spend more time on high-level analysis, strategic thinking, and client communication.

In fact, banks like Goldman Sachs, JPMorgan, and Barclays are already implementing AI tools to speed up their deal analysis processes. These tools help extract insights from thousands of documents, detect market patterns, and even generate reports—making them an invaluable part of modern financial operations.

If you choose to learn investment banking in Chennai, you’ll want to gain exposure to this AI-enhanced workflow. Courses that teach both the fundamentals of financial modeling and how to work with modern tools are the ones that will truly prepare you for success.

Why Learning Investment Banking in Chennai Is a Smart Move

Chennai is fast emerging as one of India’s most promising financial and technology hubs. With the presence of global banks, fintech startups, and analytics companies, it offers an ideal ecosystem to study and grow in this evolving field.

When you learn investment banking in Chennai, you benefit from an environment that supports both academic rigor and practical, industry-relevant training. More importantly, many of the institutes here are already integrating AI, financial automation, and data analytics into their programs—giving you a competitive edge in the job market.

Skills You Need to Thrive in the AI-Driven Finance World

To stand out in the age of AI-powered financial modeling, aspiring investment bankers should develop a mix of traditional and modern skills.

Start with a strong foundation in accounting and financial statement analysis. These fundamentals still form the core of every model, regardless of how much technology is involved.

Next, focus on mastering Excel—not just for formulas, but also for advanced functions, automation, and integration with AI tools. Tools like Excel Copilot and AI-enhanced plugins are making Excel smarter, and knowing how to use them effectively can dramatically increase your efficiency.

In addition to Excel, consider learning basic Python or financial scripting. These skills allow you to automate repetitive modeling tasks and create simulations. While you don’t need to become a full-fledged programmer, understanding how to work with AI in a hands-on way will help you collaborate better with tech teams and stand out in interviews.

Just as important is developing the ability to prompt AI tools like ChatGPT for financial insights. Prompt engineering—knowing how to ask the right questions—has become a powerful skill for modern analysts.

Finally, critical thinking and business acumen are key. AI can process and summarize data, but it’s up to you to interpret those results, apply them to real-world scenarios, and make strategic decisions based on them.

The Bottom Line: Be the Analyst AI Wants to Work With

AI is not here to replace you—it’s here to assist you. But that also means the expectations are higher. Employers are now looking for analysts who understand both finance and technology—people who can think strategically, model accurately, and collaborate with machines.

If you’re planning to learn investment banking in Chennai, don’t just look for a course that teaches Excel or valuation methods. Look for a program that prepares you for the future—where AI is not the enemy, but your most powerful teammate.

The future of investment banking will be shaped by those who embrace this shift early. And Chennai, with its blend of financial opportunity and tech innovation, is one of the best places to start.