analyst, techie, dreamer, adventurer, wanderer, reader
Mumbai, India
Joined September 2011
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Vivek Chennuri retweeted
I curated 125 free math textbooks, written by the mathematicians who teach the courses and shared on their own university pages. No sign-up, no paywall, no pirated copies. From high school algebra to graduate analysis. 👇
abakcus.com/book-lists/free-…
Vivek Chennuri retweeted
AIR 1 in IIT-JEE. AIR 1 in AIEEE. Gold Medalist at the International Physics Olympiad. No other topper in the history of the IITs has ever secured that triple crown.
Yet, 2 years later, he quit.
In 2006, an eighteen-year-old Chandigarh boy, Raghu Mahajan, claimed the undisputed trophy of the Indian education system: Computer Science at @iitdelhi
On paper, the script was already written. Coast through IIT, dominate placements, take a Silicon Valley offer, and end up as the face on a newspaper ad for a 2 Crore package.
But he was not playing that game.
Inside IIT Delhi, Mahajan found a campus life not very inspiring when it came to research. It was an optimization engine built to funnel the country's best minds into high-paying jobs. Not passion for learning.
So he dropped out. He flew to Boston and transferred to MIT to study pure physics and mathematics.
Then PhD at Stanford. Then, as a postdoc at Princeton, he co-authored foundational work with Juan Maldacena on the Page curve and black hole entropy, helping explain how information survives at the very edge of the universe.
In 2024, after sixteen years abroad, he came back to India. He now works at the International Centre for Theoretical Sciences in Bengaluru as a humble researcher.
His exact skill set is worth a fortune in finance and big tech. But he instead chose to use it on the hardest questions that no one pays him to answer. His prize is in the pursuit of those answers.
And that is a flex money can't buy
Vivek Chennuri retweeted
👩🏫 UNA PROFESORA DE STANFORD EXPLICA CÓMO DOMINAR LOS PROCESOS DE DECISIÓN DE MARKOV
Una clase de 83 minutos, gratuita y directamente desde Stanford.
🧠 En la lección aprenderás:
• Diferencias entre problemas de búsqueda y entornos estocásticos.
• Evaluación de políticas y recurrencias de valores Q.
• Cómo funciona la iteración de valores.
• Límites de convergencia en grafos cíclicos.
Todo explicado desde los fundamentos de los Markov Decision Processes (MDP).
🔥 Si estás aprendiendo inteligencia artificial, aprendizaje por refuerzo o toma de decisiones algorítmica, es una clase para guardar.
📌 Guárdala y mírala cuando tengas tiempo.
Una joya gratuita de Stanford.
Vivek Chennuri retweeted
A proof without words is not proof without reasoning. It is reasoning made visible.
Vivek Chennuri retweeted
MIT published a brutally honest report on what AI is doing to students.
A committee of professors and students spent five months studying how AI changed learning on campus, and the findings read like a warning to every university on the planet.
Study groups are disappearing. Office hours are emptying out. Problem sets and take-home exams no longer prove anything, because AI can produce credible solutions to almost any written assignment in the undergraduate curriculum. Students who lean on chatbots lose mastery and confidence, and some slip into what the report calls cognitive surrender, reaching for AI at the first hint of struggle.
The numbers are rough. 46 percent of surveyed MIT undergrads use LLMs daily. 90 percent worry about their own overreliance. Undergrads who feel AI makes them replaceable now outnumber those who feel it makes them capable.
The committee's answer surprised me. They refused to fight AI with surveillance. The report calls AI detectors unreliable, says lockdown browsers feel like spying, and warns that policing students builds a classroom atmosphere of mutual distrust.
Instead, MIT wants to rebuild education around the things AI can't replace. That means oral exams, semester portfolios, in-person project work, and a required social component in every subject. The report even floats the idea of rethinking grades entirely, since without a GPA to optimize, much of the incentive to cheat with AI evaporates.
The committee warns professors against replacing undergrad research assistants with AI agents just because they're cheaper, because a university exists to grow people, not output.
The most famous tech school on earth admitted the machines broke its way of teaching. Its answer is more humans, not more software.
Vivek Chennuri retweeted
"Machine Learning Foundations, Volume 1: Supervised Learning" - available at amzn.to/4syhPal
𝓑𝓮𝓷𝓮𝓯𝓲𝓽𝓼:
💠Master the key concepts of supervised machine learning, including model capacity, the bias-variance tradeoff, generalization, and optimization techniques
💠Implement the full supervised learning pipeline, from data preprocessing and feature engineering to model selection, training, and evaluation
💠Understand key learning tasks, including classification, regression, multi-label, and multi-output problems
💠Implement foundational algorithms from scratch, including linear and logistic regression, decision trees, gradient boosting, and SVMs
💠Gain hands-on experience with industry-standard tools such as Scikit-Learn, XGBoost, and NLTK
💠Refine and optimize your models using techniques such as hyperparameter tuning, cross-validation, and calibration
💠Work with diverse data types, including tabular data, text, and images
💠Address real-world challenges such as imbalanced datasets, missing data, and high-dimensional inputs
Vivek Chennuri retweeted
Problem Solving Through Recreational Mathematics (Math Games & Puzzles): amzn.to/4sFvJXY
Vivek Chennuri retweeted
Grigori Perelman is a Russian mathematician known for his work in geometry and topology. In 2002, he published work that proved the Poincaré conjecture, a major problem in topology.
In 2003, he reportedly stopped working in mathematics and lived a private life with his mother in Saint Petersburg.
In 2006, Perelman was awarded the Fields Medal for his contributions to geometry, particularly his work on Ricci flow. He declined the medal, saying that he was not interested in money or fame and did not want public attention.
In 2010, he was awarded the Clay Millennium Prize of one million dollars for proving the Poincaré conjecture. He also declined this prize.
Perelman became known for avoiding publicity and for rejecting major awards and prize money.
Vivek Chennuri retweeted
For 24 years, Mirka Federer stayed in the Background.
But her Speech inducting Roger Federer into the International Tennis Hall of Fame just broke the internet. 😭🐐
Heart Touching.
The Most Beautiful Video on my Timeline Today !!!
This is What a Family Looks Like. Respect. Awe. Support.
And Most Importantly, Cherishing those Wonderful Moments.
Share Widely. But After Watching ❤️
#FI
Vivek Chennuri retweeted
Ewin Tang, born in 2000, is a gifted computer scientist whose teenage work quietly rewrote what we thought we knew about quantum speedups.
At 18, as a UT Austin undergraduate advised by Scott Aaronson, she designed a classical algorithm for recommendation systems that ran nearly as fast as the best quantum version, proving ordinary computers could still surprise us.
She later dequantized other machine-learning and linear-algebra tasks, then earned her PhD at the University of Washington. Her insights won Forbes 30 Under 30 recognition and the 2025 Maryam Mirzakhani New Frontiers Prize.
Now a Miller Fellow at Berkeley and soon a Princeton professor, she keeps asking where quantum computers truly help and where clever classical ideas suffice. Her story shows that one clear-eyed student can change the conversation in mathematics and computation.
Vivek Chennuri retweeted
This man teaches at a community college in California.
His salary: around $800,000 a year.
The engineers who passed calculus because of him: $1,800,000 to start.
He has more calculus students than Harvard, MIT, and Stanford combined.
This is Professor Leonard's Calculus 2, Lecture 6.2. Free on YouTube.
Professor Leonard has taught calculus on YouTube for over a decade. His channel has millions of subscribers across 150 countries. Every major university has students who watch him the night before their exam.
Then the concept.
An inverse function is a machine that undoes another machine. If a function takes 2 and gives you 8, the inverse takes 8 and gives you back 2. Finding an inverse means switching every x and y in the equation and solving for y again. The graph flips across the line y = x like a mirror.
Then the problem.
Sometimes it is easy to find the inverse. Sometimes it is impossible to write it explicitly. A function like 3π sin x + sin x cannot be solved for x with algebra. You have to think. What angle makes the whole thing equal to 1? You work backwards through the unit circle until the answer appears.
Then the shortcut.
If you want the derivative of an inverse at a point, you do not need the inverse itself. You only need the derivative of the original function. The formula: the derivative of the inverse at a point equals 1 divided by the derivative of the original function evaluated at the switched point. The inverse flips the coordinates, so you flip where you plug in.
Watch the moment he shows why G prime of 8 equals 1/12 without ever writing the inverse function.
Every engineering student memorizes the derivative rules. Professor Leonard's lecture is the one that shows why the inverse derivative formula is just those same rules run backwards.
A software engineer at a semiconductor company in Austin said Professor Leonard's channel is the reason she passed Calculus 2 on her second attempt. She graduated, joined the company, and now makes $165,000 a year.
Bookmark this and watch later - after this lecture every inverse problem on your exam will feel like a question you already answered.
Vivek Chennuri retweeted
STANFORD'S COMPLETE LLM COURSE IS FREE 📌
If you're serious about Generative AI, LLM Engineering, or Agentic AI, this is a great course to save.
COURSE OUTLINE
1. Transformers
youtube.com/watch?v=Ub3GoFaU…
2. Transformer-Based Models & Tricks
lnkd.in/gHTY27uH
3. Large Language Models
youtube.com/watch?v=Q5baLehv…
4. LLM Training
youtube.com/watch?v=VlA_jt_3…
5. LLM Tuning
lnkd.in/ga5tcchy
6. LLM Reasoning
lnkd.in/guBgZVRt
7. Agentic LLMs
youtube.com/watch?v=h-7S6HNq…
8. LLM Evaluation
youtube.com/watch?v=8fNP4N46…
9. Recap & Current Trends
youtube.com/watch?v=Q86qzJ1K…
OFFICIAL COURSE SYLLABUS
cme295.stanford.edu/syllabus…
The official Stanford syllabus confirms all 9 lectures and their topics, including RAG, tool calling, agents, LLM evaluation, RLHF, LoRA, reasoning, and current trends.
If you already know the basics, pay extra attention to:
Lecture 7 → Agentic LLMs
Lecture 8 → LLM Evaluation
Vivek Chennuri retweeted
Don't waste 2 years learning to become an AI agentic engineer in 2026.
Andrew Ng, the godfather of AI, gave the complete playbook to become one from scratch.
1 hour course. Free:
• 00:00 - AI agent basics
• 12:12 - AI Agentic workflows & design patterns
• 53:27 - Practical tips for building AI agents
• 1:20:30 - self-improving AI agent loops
• 1:30:19 - multi-agent AI systems
I watched it last night.
Halfway through, I realized I could get into Anthropic in weeks, not years.
Bookmark now. Watch it. Then build your own AI agent
Vivek Chennuri retweeted
"Mathematical Thinking — For People Who Hate Math: Level Up Your Analytical and Creative Thinking Skills. Excel at Problem-Solving and Decision-Making"
Get started at amzn.to/4byYxv7
“Mathematics
Vivek Chennuri retweeted
She is 18 built a game with 4 agents and sold it to Microsoft for $2.4M - and at Stanford shared how to repeat it from scratch:
00:09 - how Opus 5 in Claude Code built a game making $100k a month
23:34 - 4 agents replaced a developer team worth $200k a year
59:47 - how to build your first game from scratch in 60 minutes
after watching I gave Claude Code the game idea I had been holding for 2 years - in 60 minutes it already existed in the App Store and was making money.
save & watch - the article below shows how 4 agents and Claude Code replace a developer team and build a game Microsoft will buy for $2.4M.
Vivek Chennuri retweeted
🚨Leave Netflix tonight.
Watch this 2 h 34 min Stanford class.
It's the clearest, most complete, and brutally honest explanation out there on how ChatGPT and Claude are really built.
From Tokenization and BPE to the Transformer architecture, the training pipeline, and the next-token decoder. No fluff. No marketing. Just the truth.
Doesn't matter if you've never touched a line of AI code or if you spend your days launching Agents: by the end, you'll suddenly connect a ton of pieces you've been trying to fit together for years.
The real core boils down to this:
How text turns into numbers the model can "eat" (BPE tokenization)
The sole mission of a language model: predict the next token
How the Transformer uses Attention so tokens can pass messages to each other
In training, the NLL loss pushes the probability of the entire sequence
In generation, the decoder builds the response token by token
The big-picture view that takes most people years to form… this class delivers it to you complete in one sitting.
Free up your time.
This could be, no exaggeration, the most valuable class you watch this month.
