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AI Engineering Cohort registrations are open. The cohort is an 8-week program for professionals who want to build production-grade AI applications. The cohort will run from 6th June to 26th July 2026. ------------ Main topics: Week 1. Basics of LLMs: Tokenization, Vectorization, Attention Week 2. RAG components & architecture Week 3. Advanced RAG & Evals Week 4. RAG Architectures & Specialized Types Week 5. Agentic Systems, ReAct & tool calling Week 6. Multi-Agent Systems, Orchestration Week 7. Context Engineering, Memory & Evals Week 8. Capstone Project: Build your own AI Project ---------------- Primary Audience: Software / AI Engineers Number of Sessions: 16 Live Classes + 7 Networking Sessions Involves Coding: Yes Classes: Live Instructor: Tanishq Singh Pricing: ₹1,00,000 / $1100 ---------------- This cohort is for working professionals looking to gain job-relevant AI capabilities. You can find the detailed syllabus on the website. Register here: https://aiengg.dev Wishing you a great 2026!

Moving Beyond the Magic Box For the past 8 weeks, I’ve been deep-diving into the world of LLMs as part of Gaurav Sen's AI Engineering Cohort. As someone who learns best by visualizing how systems fit together, this has been incredibly rewarding. We’ve been peeling back the layers of the Transformer architecture—moving past the high-level hype to understand exactly how data flows through attention heads, how causal masking works in practice, and how to build these components from the ground up. There is a unique satisfaction in seeing a concept from a research paper come to life in code. It transforms "AI" from a mysterious black box into a tangible engineering challenge that can be optimized, debugged, and scaled. The First 8 Weeks: I've focused heavily on the foundations—mastering tokenization, vector embeddings, and the internal mechanics of self-attention. Getting these fundamentals right has changed how I approach building AI-driven features. The Next 8 Weeks: I’m looking forward to the next half of the program, where the focus shifts toward production-grade systems: multi-agent orchestration, advanced RAG patterns, and fine-tuning strategies. Building with GenAI is one thing, but engineering robust, reliable AI systems is the real frontier. Excited to see where the next 8 weeks take me! #AIEngineering #GenerativeAI #ContinuousLearning #MachineLearning

Learning how LLMs actually work has been eye-opening. Gaurav Sen course gives a solid, no-fluff overview of the foundations. Good stuff for any software engineer building with AI. Building / Learning in Public - let's see how this goes. Share if you if have any suggestions for resources?

THE APRIL END OF MONTH SALE ENDS IN 48 HOURS ! 🚀 Don't let your skills plateau. Secure 70% OFF our core engineering toolkits and master the architecture behind the world's most scalable systems. Course Previews: 1. System Design: https://lnkd.in/dQuy-fiD 2. AI Engineering: https://lnkd.in/ds9amkDx 3. Low Level Design: https://lnkd.in/gKVpaC9F 🎟Coupon Code: APRIL70 ⏰Sale ends MAY 1st at 23:59:00 IST.

Part 1 - I’ve been learning System Design lately. I followed Gaurav Sen’s explanation and worked through Instagram's system design. 🔹 Precomputed Feeds (Fan-out on Write) Instead of generating a feed every time a user opens the app (which is slow), a system that pushes posts to user feeds when they are created was designed. 🔹 High-level flow • User hits API → Gateway (reverse proxy) • Request routed via load balancer (hashing userID) • Post Service stores content • Feed Service precomputes feeds • Follow Service fetches followers • Post gets distributed into each follower’s feed queue 🔹 Caching Layer Feeds are cached (LRU-based) → fast retrieval when user opens the app 🔹 Push vs Pull trade-off • <10K followers → Push model (fan-out on write) • >1M followers → Pull model (fan-out on read) • Hybrid approach for celebrities (batching / partial pull) 🔹 Why this matters Naively doing: getFollowers(user) → fetch posts → merge → paginate …quickly becomes inefficient at scale (high latency + compute heavy) What I found interesting is how something as simple as a “feed” involves: caching strategies, distributed systems, and trade-offs between read vs write optimization. Still learning, but breaking these systems down step by step is helping me understand why these design decisions exist, not just what they are. If you’re learning System Design, I’d definitely recommend sketching things yourself, it makes a big difference.

April is ending. Your 70% discount is here. We’re dropping a sitewide 55% Flat Discount for the next 96 hours. Want to hit 70%? > Stack an additional 15% off when you bundle two or more courses. The Stack: 1️⃣ System Design: Distributed systems mastery. 2️⃣ AI Engineering: LLMs, Agents, and RAG. 3️⃣ LLD: Scalable patterns and clean code. Coupon Code: APRIL70 Sale ends May 1st at 12:00:00 IST.

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We are starting with AI Training for Teams. If your engineering, product, or business teams are trying to figure out how to actually use AI at work, you can book a training program with us. Expected outcomes: → Learn AI terminology to communicate effectively across teams → Build effective agentic systems → Evaluate AI systems for production use → Get the most out of tools like Claude Code, Codex, etc... If you're a team lead, please note your interest here: https://lnkd.in/gRSxk3gf If not, forward this to your manager or HR (slide into their DMs :p). Cheers!