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🔥 Government set to name ~8 Indian teams for foundational model incentives next week – second-round beneficiaries may include BharatGen; GPU access remains tight as only ~17,374 of planned 34,333 GPUs are installed so far. 🤔 Why It Matters – More

I fine-tuned 3 models this week to understand why people fail. Used LLaMA-2-7B, Mistral-7B, and Phi-2. Different datasets. Different methods (full tuning vs LoRA vs QLoRA). Here's what I learned that nobody talks about: 1. Data quality > Data quan

This is crazy 🤯🔥! Mumbai-based AI startup Neysa has raises $1.2 billion funding, around ₹10,897 crore($600M equity + $600M debt) led by Blackstone at the valuation of $1.4 billion 💵. The Startup founded in 2023 by Sharad Sanghi and Anindya Das.

"A Survey on Post-Training of Large Language Models" This paper systematically categorizes post-training into five major paradigms: 1. Fine-Tuning 2. Alignment 3. Reasoning Enhancement 4. Efficiency Optimization 5. Integration & Adaptation 1️⃣ Fin

🚨 Open AI is an Wrapper👀🤯 Hot take, but let’s break it down logically: OpenAI is not a full-stack AI company — it’s a high-level wrapper over Azure and NVIDIA. Here’s why that matters 👇 🔹 1. Infra Backbone = Microsoft Azure Almost 90%+ of Op

SLM vs LLM — which AI model is best for you? I’ve explained both in simple steps below. 𝗦𝗟𝗠 (𝗦𝗺𝗮𝗹𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹) (𝘴𝘵𝘦𝘱-𝘣𝘺-𝘴𝘵𝘦𝘱) Lightweight AI models built for speed, focus, and on-device execution. 1. 𝗗𝗲𝗳𝗶𝗻𝗲

From Emotional AI to Enterprise Infrastructure: The Story of Friday AI I don’t know whether to call it a phobia or obsession, but when it comes to my work, I chase correctness relentlessly. I’m not perfect, but I’m consistent. Where It Started Fri

LLM Post-Training: A Deep Dive into Reasoning LLMs This survey paper provides an in-depth examination of post-training methodologies in Large Language Models (LLMs) focusing on improving reasoning capabilities. While LLMs achieve strong performance

💡An Idea to Change the Game for AI Startups: Making AI Processing Faster, Cheaper, and Effortless Running AI models like ChatGPT, DALL·E, or AlphaCode is a computing monster—they need massive power to function, which makes them expensive to operate