Anant Maheshwari

How LLMs Work Is Not Magic Anymore A few weeks back I had taken a deep dive into the world of Large Language Models (LLMs) — and it’s been truly fascinating to unpack what goes on under the hood of models like GPT, Claude, and others. What once felt like “magic” now feels like brilliant engineering and elegant math. Through studying the Transformer architecture, I’ve developed a clear understanding of how these models actually think and learn to generate coherent, human-like text. Here’s what I’ve explored in detail 👇 🔹 Encoder–Decoder Architecture (Seq2Seq Models) – Understanding how data flows through the encoder to create context-rich representations, and how the decoder turns that into meaningful output. 🔹 Tokenization & Embeddings – How text is broken down into tokens and represented as dense vectors in a high-dimensional space. 🔹 Self-Attention Mechanism – The core innovation that allows models to understand relationships between words across long contexts. 🔹 Transformers – The building blocks of modern LLMs, replacing recurrence with attention for parallelism and scalability. 🔹 ReAct Framework – Combining reasoning and action to enhance the model’s problem-solving and decision-making capabilities. 🔹 Mixture of Experts (MoE) – The concept of dynamically routing tasks to specialized “expert” subnetworks to improve efficiency and performance. The deeper I go, the more I appreciate how systematic, scalable, and elegant these architectures are. ✨ What’s most exciting is that LLMs are no longer just black boxes to me — I can now understand and explain the “why” behind their behavior. #AI #MachineLearning #LLMs #Transformers #DeepLearning #NLP #ArtificialIntelligence #LearningJourney

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'BharatGen' - Indian Institute of Technology, Bombay's AI venture. BharatGen's ambition is not just to build LLMs, but to build ones that sound and think like India. It's strength said Prof. Ganesh Ramakrishnan, lies in training systems on home-grown datasets and Indian languages. Rishi Bal Milind Atrey Shireesh Kedare Shashi Shekhar Vempati Abhay Karandikar Abhishek Singh

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Bengaluru, I’m really excited to see you soon. 🇮🇳 Mid-2025, Arvind Narayanamurthy and I met through a mutual friend and instantly connected over a shared belief: AI education needs to be practical, technical, and grounded in real industry work. There was no shortage of theory or surface-level content. What felt missing were hands-on, deeply technical workshops that actually help people build agentic systems. So we ran a pilot - The Gen Academy. Over the last six months, we’ve trained close to 2,000 professionals through workshops on white-coding with AI tools, context engineering, MCP, agentic systems, RAG architectures, and frameworks like LangChain and LangGraph. The response made it clear that professionals are hungry to seriously upskill and be job-ready. Now we’re bringing this to Bengaluru. AI Launchpad is happening with Nas.io and The Hub Bengaluru, and it’s a dense, hands-on evening. Here’s what to expect: ✦ AI career and upskilling insights going into 2026 ✦ A panel with AI professionals from Google, Adobe, and more ✦ Exclusive book giveaways and resources ✦ A What’s Your Worth book signing ✦ Quizzes and nerdy hoodie giveaways The first batch of tickets sold out fast. More seats are now open. Register here: https://lnkd.in/eRR7Zfj8

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Shantanu Narayen’s 18 year tenure as CEO at Adobe is a reminder of how rare long-term stewardship has become in Silicon Valley. During his time with the company, the company’s stock increased more than 6x and its business model fundamentally changed. The most important decision of his tenure was moving Adobe from selling boxed software like Creative Suite to a subscription platform through Creative Cloud. At the time it was controversial. Today, it is considered one of the most successful SaaS transitions in the history of enterprise software, turning Photoshop, Illustrator, and Acrobat into one of the most profitable recurring software ecosystems ever built. The respect he commands across the industry says a lot about how difficult that transformation actually was. Microsoft CEO Satya Nadella even posted publicly congratulating Narayen on a “legendary run,” praising both his leadership and the way he expanded what creators and businesses could do with software. In an industry that is often intensely competitive, that kind of recognition from peers is not given lightly. There are only a few CEOs in the world who truly get to define one era of software and then help set the stage for the next. Narayen did it by turning Adobe into a SaaS powerhouse. However, the next leader will have to answer a harder question. What does a creative software company look like when AI becomes part of the creative process itself? #Adobe #Leadership #SaaS #AI #Technology

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I've been following the context graphs debate with great interest. Jaya Gupta and Ashu Garg's "trillion-dollar" thesis, Prukalpa ⚡'s perspective on heterogeneity, Arvind Jain connecting it to transactive memory systems. These are important ideas. And the problem they're highlighting is real. Enterprises desperately need better ways to capture how decisions get made. Having spent years trying to build exactly these systems, I want to share what I've learned about why this is so hard. Lesson 1: You Need to Own the Interface At Nutanix, I worked on Intentful APIs for cloud infrastructure. The idea was to capture not just what changed, but why. We learned something fundamental: this only works if you own the entire interface. Kubernetes can do this because everything flows through its control plane. But enterprise environments are messier. States get mutated directly in underlying layers. Someone SSHs into a box. A quick database fix happens at 2am before a board meeting. This isn't bad behavior. It's reality. But it means any context graph is working with incomplete information. Lesson 2: Decisions Bottom Out at Human Judgment At Kognitos, I built exception handling systems which captured "decision traces". Decision-making is deeply recursive. Ask why a discount was approved. Then why that exception exists. Keep asking why. Eventually, you arrive at: "Because someone with experience made a judgment call." That's not a failure of documentation. That's how organizations work. Decisions happen under time pressure, with incomplete information, based on a team's evolving understanding. Lesson 3: The "How" is Often Private Recently at SoraNovaAI, I built knowledge base systems for a venture capital firm. What I discovered is that people often keep the "how" of their decision-making private. Not out of malice, but because it's genuinely their edge. A senior investor's pattern recognition. A sales leader's intuition about which deals to push. These are competitive advantages tied to personal goals. Context graphs assume everyone wants their decision-making captured. The reality is more nuanced. I believe in the vision of better decision capture. The pain is real. Organizations lose institutional knowledge constantly, decisions get relitigated, context disappears when people leave. But different people always see different parts of the same decision. That's not a bug. It's how organizations work. What I've learned: - Design for incomplete context, not perfect capture - Focus on domains where incentives align with transparency - Accept that the last mile is human judgment - Celebrate progress over perfection The real world is messy. The opportunity is in building systems that embrace the mess and still make things better. I'd love to hear from others who've navigated this. What's worked? What hasn't? #contextgraphs #enterpriseai #aiagents

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In my last blog, I covered Distributed Inference Serving for LLMs. This time, I’ve written about training, breaking down DDP vs FSDP and what to use when for large-scale deep learning. Link: https://lnkd.in/gcgtSZEk #AI #DeepLearning #DistributedTraining #DDP #FSDP #PyTorch #MLTraining #Scalability #MLOps #GPUComputing #AIInfrastructure

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H1B Visa fees 100,000 Dollar. Time to awake indians. No other way then to get out of delusion of development. We should focus more on economy then cultural activities going on now a days. This visa row is indication of following 1. AI is really on the way to destruct and change IT sector forever. 2. Jobs placements will be lower then expected. 3. Even if this visa fees is reduced then also there will be an uncertainty on the AI changing the world order. 4. Narratives build that now india will be great again, is false i think. 5. Only way to avoid heavy losses is to truly invest in AI research.

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