Dr. Sambit Praharaj

An experience of a lifetime when you end something significant! PhD done :) Thank you everyone, thanks to my supervisors and the committee for this awesome experience! Especially, my supervisors made this time period a memorable one which will be etched in my memory forever.

BREAKING: MIT just mass released their Al library for free. (Links included) I went through these and honestly... this is better than most paid courses I've seen. Here's the full list of books: Foundations 1. Foundations of Machine Learning Core algorithms explained. Theory meets practice. 2. Understanding Deep Learning Neural networks demystified. Visual explanations included. 3. Machine Learning Systems Production-ready architecture. System design principles. Advanced Techniques 4. Algorithms for ML Computational thinking simplified. Decision-making frameworks. 5. Deep Learning The definitive textbook. Covers everything deeply. Reinforcement Learning 6. RL Basics (Sutton & Barto) The classic. Agent training fundamentals. 7. Distributional RL Beyond expected rewards. Advanced theory. 8. Multi-Agent Systems Agents working together. Coordination and competition. 9. Long Game Al Strategic agent design. Future-focused thinking. Ethics & Probability 10. Fairness in ML Bias detection. Responsible Al practices. 11. Probabilistic ML (Part 1 & 2) Links: lnkd.in/gkuXuexa Most people pay thousands for bootcamps that teach half of this. Bookmark it. Start anywhere. Just start. Repost for others Follow for more insights on Al Agents. MIT's books on Al Foundations 1. Foundations of Machine Learning - lnkd.in/gytjT5HC 2. Understanding Deep Learning - lnkd.in/dgcB68Qt 3. Machine Learning Systems - lnkd.in/dkiGZisg Advanced Techniques 4. Algorithms for ML - algorithmsbook.com 5. Deep Learning - lnkd.in/g2efT6DK Reinforcement Learning 6. RL Basics (Sutton & Barto) - lnkd.in/guxqxcZZ 7. Distributional RL - lnkd.in/d4eNP-pe 8. Multi-Agent Systems - marl-book.com 9. Long Game Al - lnkd.in/g-WtzvwX Ethics & Probability 10. Fairness in ML - fairmlbook.org 11. Probabilistic ML (Part 1) - lnkd.in/g-isbdjj 12. Probabilistic ML (Part 2) - lnkd.in/gJE9fy4w

Best YouTube Channels To Learn AI in 2026 (No BS) 1. Fundamentals – 3Blue1Brown 2. Deep Learning – Andrej Karpathy 3. AI Research – Yannic Kilcher 4. Practical AI – AssemblyAI 5. LLMs – AI Explained 6. ML Theory – StatQuest 7. Papers Simplified – Two Minute Papers 8. GenAI – Matthew Berman 9. AI Agents – Nicholas Renotte 10. Applied ML – Krish Naik 11. PyTorch – Aladdin Persson 12. Math for ML – Serrano Academy 13. Industry Insights – Lex Fridman 14. Real-world AI – DeepLearningAI

Automates poster generation from papers github.com/Paper2Poster/P…

🚨 BREAKING: India’s AI startup Sarvam AI has open-sourced its 30B and 105B AI models for developers.

Google dropped another banger! PaperBanana is an agentic framework that generates publication-ready academic illustrations from methodology descriptions. no manual design, no Figma, just your method section and a caption.

RAG is broken and nobody's talking about it 🤯 Stanford just dropped a paper on "Semantic Collapse," proving that once your knowledge base hits ~10,000 documents, semantic search becomes a literal coin flip. Here is why your RAG is failing: Past 10,000 documents, your fancy AI search basically becomes a coin flip. Every document you add gets turned into a high-dimensional embedding. At a small scale, similar docs cluster together perfectly. But add enough data, and the space fills up. Distances compress. Everything looks "relevant." It’s the curse of dimensionality. In 1000D space, 99.9% of your data lives on the outer shell, almost equidistant from any query. Stanford found an 87% precision drop at 50k docs. Adding more context actually makes hallucinations worse, not better. We thought RAG solved hallucinations… it just hid them behind math. The fix isn’t re-ranking or better chunking. It’s hierarchical retrieval and graph databases.

Apple has just published a paper with a devastating title: *The Illusion of Thinking*. And it's not a metaphor. What it demonstrates is that the AI models we use every day - yes, ones like ChatGPT - don't think. Not one bit. They just imitate doing so. Let me explain: 🧵👇

20 YouTube channels that teach AI better than most CS degrees in 2026: 1. Andrej Karpathy Deep, intuitive walkthroughs of neural networks and modern LLMs @AndrejKarpathy" target="_blank" rel="nofollow noopener">youtube.com/@AndrejKarpathy 2. 3Blue1Brown Visual intuition for math, linear algebra, and neural networks @3blue1brown" target="_blank" rel="nofollow noopener">youtube.com/@3blue1brown 3. StatQuest with Josh Starmer Clear, friendly explanations of statistics and ML fundamentals @statquest" target="_blank" rel="nofollow noopener">youtube.com/@statquest 4. Stanford Online University-grade ML and AI lecture series (Andrew Ng, CS229, etc.) @stanfordonline" target="_blank" rel="nofollow noopener">youtube.com/@stanfordonline 5. sentdex Practical machine learning and Python projects @sentdex" target="_blank" rel="nofollow noopener">youtube.com/@sentdex 6. Yannic Kilcher Deep dives into ML and AI research papers @YannicKilcher" target="_blank" rel="nofollow noopener">youtube.com/@YannicKilcher 7. MIT OpenCourseWare Rigorous academic courses on ML, AI, and applied mathematics @mitocw" target="_blank" rel="nofollow noopener">youtube.com/@mitocw 8. Siraj Raval High-level overviews and motivation around AI concepts Link: @SirajRaval" target="_blank" rel="nofollow noopener">youtube.com/@SirajRaval 9. DeepLearningAI Structured learning paths for deep learning and generative AI @DeepLearningAI" target="_blank" rel="nofollow noopener">youtube.com/@DeepLearningAI 10. Two Minute Papers Fast, accessible summaries of cutting-edge AI research @TwoMinutePapers" target="_blank" rel="nofollow noopener">youtube.com/@TwoMinutePape…

Systematic reviews in minutes to hours using artificial intelligence medrxiv.org/content/10.648… (caveat: thinly veiled advertisement for scholara.ai)

I scraped every single NotebookLM prompt that blew up on X, Reddit, and academic corners of the internet. Turns out most people are using NotebookLM like a fancy note-taker. That's insane. It's a full-blown research assistant that can compress 10 hours of analysis into 20 seconds if you feed it the right instructions. Here's what actually works:

BREAKING: AI can now build financial models like Morgan Stanley analysts (for free). Here are 12 Claude prompts that replace $200K+/year investment banking jobs (bookmark this thread 🧵)

Instead of watching a 2-hour movie, watch Anthropic’s 7-hour Claude Code masterclass in just 27 minutes

Google just dropped another banger 🤯 It’s called PaperBanana, a new tool that generates publication-ready academic illustrations directly from your methodology text. 100% Free.

Most people talk about AI like it’s one single thing. It’s not.. AI is a stack and once you see it this way, everything clicks. Here’s the mental model 👇 1️⃣ Classical AI (the foundation) Where it all began: • Logic & reasoning • Rule-based systems • Expert systems Smart, but rigid. It followed instructions it couldn’t learn. 2️⃣ Machine Learning Instead of rules, we gave machines data: • Supervised & unsupervised learning • Classification & regression • Reinforcement learning Now systems could improve from experience. 3️⃣ Neural Networks Inspired by the human brain: • Perceptrons • Hidden layers • Activation functions • Backpropagation This unlocked real pattern recognition. 4️⃣ Deep Learning Neural networks but deeper and more powerful: • CNNs for vision • RNNs & LSTMs for sequences • Transformers for scale This is where machines learned to see, hear, and understand. 5️⃣ Generative AI Models that don’t just analyze they create: • LLMs • Diffusion models • VAEs • Multimodal systems Text, images, audio, video generated from learned patterns. 6️⃣ Agentic AI (the frontier) The biggest shift yet: • Memory • Planning • Tool usage • Autonomous execution This is where AI stops being a tool and starts acting like a digital worker. 👇 The real takeaway Each layer doesn’t replace the one below it it builds on it. Understanding this stack is the difference between: • Chasing AI hype • Actually building systems that work AI isn’t magic. It’s architecture. And the future belongs to those who understand how these layers connect.

Google isn’t trying to win the AI race. They’re trying to own the entire AI Agent ecosystem. While everyone argues ChatGPT vs Claude, Google quietly built: Models → Gemini Pro, Flash, Deep Think, Gemma Design → Stitch, Whisk, Imagen Research → NotebookLM, AI Mode Video → Veo, Flow, Google Vids Coding → Antigravity IDE, Gemini CLI, Jules Agents → A2A, ADK, FileSearch API The scary part? All of these tools talk to each other. That means: 10x faster prototypes End-to-end AI workflows Production-ready agents on GCP The next AI war won’t be model vs model. It’ll be ecosystem vs ecosystem. I mapped this stack out here: gamma.app/?utm_campaign=… Save. Share. Build.

Google just dropped 145 pages documenting how researchers use Gemini to tackle scientific problems. 𝘚𝘢𝘷𝘦 & 𝘙𝘦𝘵𝘸𝘦𝘦𝘵 (𝘵𝘰 𝘩𝘦𝘭𝘱 𝘺𝘰𝘶𝘳 𝘯𝘦𝘵𝘸𝘰𝘳𝘬) A few things that stood out to me (in simple terms): - In one case, the AI was used as an adversarial reviewer and caught a serious flaw in a cryptography proof that had passed human review. That’s a very different use than “summarise this PDF.” - The model links tools from very different fields (for example, using theorems from geometry/measure theory to make progress on algorithms questions). This is where its wide reading really matters. - They don’t let the model run wild. Humans still choose the problems, check every proof, and decide what’s actually new. The model is there to suggest ideas, spot gaps, and do the heavy algebra. - Agentic loops, not just chat In some projects, they plug Gemini into a loop where it: -- proposes a mathematical expression, -- writes code to test it, -- reads the error messages, and -- fixes itself. (humans only step in when something promising appears) We are moving past the era of simple chat prompts and into a more sophisticated era of research. ⮑ If your institution is interested in hosting an AI session or a workshop, request your training here: forms.gle/dbRtc7j2W4zZyL…

Chatbot: You ask. It answers. RAG: You ask. It retrieves. It answers. RPA: You trigger. It executes a script. Agent: You give a goal. It figures out the rest. That's the difference. An agent has: → Memory (learns from interactions) → Planning (breaks down complex goals) → Tool selection (chooses what to use, not scripted) → Feedback loops (adjusts based on results) → Multi-agent coordination (delegates to specialists) Most "agents" in production are RAG pipelines with a for-loop. Real agentic AI has an orchestrator that thinks, deciding which tools, which sub-agents, which approach, and when to change course. If your system can't change its own plan mid-execution, it's not an agent. It's automation with an LLM inside.

OpenAI, Google, and Anthropic just published guides on: • Prompt engineering • Building agents • AI in business • 601 AI use cases 9 of the best guides you can't miss:

NotebookLM FULL COURSE 4 HOURS (Build & Automate Anything)

🚨BREAKING: Google just dropped another hit! It's called PaperBanana and it generates publication-ready academic illustrations from just your methodology text. No Figma. No manual design. No illustration skills needed. Here's how it works: A team of AI agents runs behind the scenes → One finds good diagram examples → One plans the structure → One styles the layout → One generates the image → One critiques and improves it Here's the wildest part: Random reference examples work nearly as well as perfectly matched ones. What matters is showing the model what good diagrams look like, not finding the topically perfect reference. In blind evaluations, humans preferred PaperBanana outputs 75% of the time. This is the recursion we've been waiting for AI systems that can fully document themselves visually. Waitlist’s open, Link in the first comment.