ankit_raj | VTogether | VTogether

ankit_raj

@ankit_raj

"Data Analyst & AI explorer based in Bangalore. Finding the signal in the noise. Always learning, always building. 📊🤖"

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Latest Updates

do cycling it gives you a lot fitness

Feb 16

my new guitar!!

Feb 16

My Journey

Milestones and updates

February 25, 2026

The Rigged Algorithm: A Data Scientist’s Case for Ownership Let’s strip the mysticism away from Data Science and Artificial Intelligence right now: there is no magic in the machine. It’s just math, compute power, and an unprecedented amount of human data. For years, I sat in air-conditioned tech parks in Bangalore, writing complex Python scripts and training machine learning models for massive corporations. From the outside, it looked like I was building the future. From the inside, I was just optimizing the exact angle at which a digital hook could catch a user's attention. I was taking raw human behavior—your clicks, your watch-time, your purchasing hesitation—and refining it into predictive models designed solely to maximize ad revenue for shareholders. When the AI boom hit in the mid-2020s, everyone was panicking about robots taking their jobs. But as someone looking directly at the datasets, I saw a much more immediate problem: creators were voluntarily feeding their life’s work into billion-dollar training models for absolutely zero equity. The legacy social media ecosystem is nothing more than the most successful unpaid data-entry operation in human history. You upload a highly researched video, an original piece of code, or an insightful essay. The platform uses your work to train its proprietary AI, serves ads against your content, and then hides your reach behind a black-box algorithm unless you pay to boost it. As a data scientist, looking at the traditional creator economy felt like auditing a deliberately corrupted spreadsheet. The math is catastrophically rigged. Why would any rational actor invest their primary capital—their time and intellectual property—into a system that arbitrarily alters its distribution algorithm and takes an extortionate cut of the gross revenue? You can't outsmart a monopoly’s algorithm; you have to opt out of their dataset entirely. That is the statistical reality that drove me to vTogether. As an analyst, a 95/5 revenue split isn't just a marketing slogan; it’s the only mathematically sound variable in an otherwise chaotic industry. It represents a fundamental shift in the architecture of the internet—from a model of extraction to a model of equity. I don't need to reverse-engineer a shadowban here because the platform's financial incentives are actually aligned with mine. Today, my channel isn't just about teaching people how to code a neural network or clean a messy CSV file. It’s about teaching statistical self-defense. I am arming my community with the data literacy they need to realize exactly how much value they are generating for big tech, and showing them the exact tools they need to reclaim it. Data might be the most valuable resource on the planet, but for the last decade, creators have just been the free labor pumping it out of the ground. The extraction era is over. It’s time we started owning the refinery.

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February 16, 2026

Patterns in the Chaos: My Evolution into Data Science My journey into the world of data didn't start with a high-powered computer; it started with a deck of cards and a fascination with probability in my family home in Bangalore. While others were interested in the game itself, I was obsessed with the hidden math behind every shuffle. I wanted to understand why things happened the way they did—was it luck, or was it a pattern I hadn't seen yet? This curiosity eventually led me away from a standard engineering path and into the deep, often messy world of Data Science and Artificial Intelligence. The early days were far from glamorous. I spent countless hours in university labs cleaning "dirty" data—dealing with missing values, broken timestamps, and corrupted files. Many of my peers found it tedious, but I saw it as a form of digital archaeology. You have to clear away the debris to find the truth underneath. I realized that data isn't just numbers on a screen; it’s a digital footprint of human behavior. However, working in the corporate world for a major tech firm in Bangalore’s "Silicon Valley" eventually felt restrictive. I was helping billion-dollar companies optimize their profit margins, but I wasn't using my skills to help real people understand the technology that was changing their lives. The transition to becoming a creator was sparked by the AI explosion of the mid-2020s. I saw so much fear and misinformation being spread about what Artificial Intelligence could and couldn't do. I decided to use my background to demystify these tools for the average person. I started building small, open-source projects—like a "Personal Budget AI" and a "Simple Health Tracker"—and documented the process. I wanted to show that data power belongs to everyone, not just the tech giants. The hardest part was learning how to speak "human" again; I had to learn how to explain a neural network without using a single line of calculus. Building a presence here on vTogether has been a strategic move for me. As someone who analyzes business models for a living, I couldn't ignore the math of the creator economy. Most platforms thrive on "black box" algorithms and high commission rates that leave creators in the dark about their own success. Finding a space that offers a transparent 95/5 split isn't just a "nice feature"—it’s a data-driven competitive advantage. It allows me to treat my research and my community as a legitimate, sustainable business. Today, my journey as ankit_raj is focused on the future of "Human-Centric AI." I use this platform to share deep-dive analyses into tech trends, host live-coding sessions, and provide templates that help people take control of their own data. I believe that the better we understand the patterns of our world, the better we can navigate our future. My story is constantly being updated with new inputs, and I’m excited to see what the next set of data reveals. Let's find the signal together.