InterviewReady

THE 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: interviewready.io/course-pag… 2. AI Engineering: interviewready.io/course-pag… 3. Low Level Design: interviewready.io/course-pag… 🎟Coupon Code: APRIL70 ⏰Sale ends MAY 1st at 23:59:00 IST.

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: 1⃣System Design: Distributed systems mastery. 2⃣AI Engineering: LLMs, Agents, and RAG. 3⃣LLD: Scalable patterns and clean code. Coupon Code: APRIL70 Sale ends May 1st at 12:00:00 IST.

Latency in your career is expensive. In 24 hours, ⚡ Upto 70% OFF SITEWIDE Secure your April discount on all courses : ✅ AI Engineering ✅ System Design (HLD) ✅ Low-Level Design (LLD) Go Live: Tomorrow, April 28th @ 10:00 AM IST. 🚀

Everyone talks about the billions spent training foundation models, but what happens when you actually deploy them? The reality is that inference accounts for up to 90% of the operational costs in production. I just published a new deep dive into the physics of LLM inference and the reality of AI Engineering. If you're building heavy RAG architectures or multi-agent workflows, managing the "KV Cache Monster" and optimizing your Time to First Token (TTFT) is the real battleground. Check out the full post where I break down: 📷 The compute-bound Prefill vs. the memory-bound Decode phases 📷 How quantization (INT4/INT8) makes 70B models viable 📷 System-level orchestration like Continuous Batching and PagedAttention 📷 Choosing the right engine for your workload (vLLM vs. TensorRT-LLM vs. SGLang) Link interviewready.io/blog/how-t…

50 questions to master the Senior AI/ML interview loop in 2026. 📷 If you're still prepping with 2017 Transformer basics, you're behind. Industry loops now focus on "compute intimacy" and hardware-aware scaling. This comprehensive curriculum covers: 📷 Mathematical Rigor: Understanding the Maths behind every techniques. 📷 Architectural Evolution: Why MLA outperforms GQA in expressive power and how ALiBi achieves length extrapolation. 📷 Training Stability: Using GRPO to remove the "Critic" model bottleneck and preventing MoE expert collapse. 📷 Inference Efficiency: Handling "GPU bubbles" with continuous batching and optimizing TTFT vs. TPS throughput. Stop prepping for the models of yesterday and start prepping for the frontier. Read more: interviewready.io/blog/top-5…

Beyond Text: A Deep Dive into the Architecture of Multimodal AI We are witnessing a fundamental shift in AI, moving from models that just "read" to models that can see, hear, and perceive the world holistically. But how do we actually bridge the gap between pixels and tokens? We just published a comprehensive technical report exploring the engineering behind Multimodal AI. This isn't just high-level fluff; we dive into the math and the architecture. In this blog, we unpack: 1.The 5 Core Challenges: Representation, Translation, Alignment, Fusion, and Co-learning. 2.Architectural Paradigms: A comparison of Dual-Encoders (CLIP), Connectors (LLaVA), and the bleeding-edge Shared Tokenization models (Chameleon). 3.The Math: Understanding Contrastive Learning and InfoNCE loss. 4.The "Modality Gap": Why alignment remains the hardest problem in the field. If you wants to build future of multimodal-AI or interested in the future of MultiModalAI, this guide is for you. Check it out here: interviewready.io/blog/Multi…

Moving from "Tutorial Hell" to Your First AI Role? start here. If you are preparing for an interview in Generative AI, Machine Learning, or Data Engineering, you know the struggle: most resources are either too basic ("What is an LLM?") or too academic ("Here is the math behind Attention mechanisms"). Real interviews sit somewhere in the middle: Engineering Trade-offs. We’ve curated a list of 50 RAG (Retrieval-Augmented Generation) Interview Questions designed for everyone entering the field. Whether you are a fresh grad or a seasoned dev pivotting to AI, these questions test your ability to build systems that actually work in production. We cover the entire lifecycle: 1. Foundations: When to use RAG vs. Fine-tuning. 2. Data Strategy: Advanced chunking & metadata filtering. 3. Retrieval: HNSW vs. IVF and Hybrid Search. 4. Safety: Preventing hallucinations and prompt injection. 5. Scale: Handling 10,000 users and optimizing latency. Don't let the "easy" questions give you a false sense of security. Test yourself against the hard ones. Read the full list here: interviewready.io/blog/top-5…

🫶 Happy Valentine's Day to our first love: large-scale distributed systems. THE YES SALE IS LIVE! 🎉 Get 55% OFF all courses: 1. System Design Course Master scalable architecture from 1 to 1M users Preview: interviewready.io/course-pag… Use coupon code: YESSALE55 2. AI Engineering Course Build production-ready AI systems with LLMs, Vector DBs, and RAG Preview: interviewready.io/course-pag… Use coupon code: YESSALE55 3. Low Level Design Course Architect clean, maintainable systems by building a Game Engine Preview: interviewready.io/course-pag… Use coupon code: YESSALE55 Don't miss this opportunity to level up your career. *Sale ends February 28th at 23:59:59 IST*

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The industry doesn't need more people who can "write code." LLMs can do that now. The industry needs people who can: 1. Identify bottlenecks before they happen. 2. Structure systems for 10x growth. 3. Architect AI-native workflows. The Republic Sale ends in 48 hours. 🇮🇳 70% OFF HLD + LLD + AI Combos. Save up to 19000 today: interviewready.io/checkout/?…