Blog post #2: How Netflix serves 200 billion hours of video every year interviewready.io/blog/how-n…

We are back! A lot has changed since we last posted :) So let's keep it light and start off with a blog on system design (Uber's matching algorithm). Cheers! interviewready.io/blog/uber-…

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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…

Why AI Systems Don’t Fail on Day One — They Fail on Day Fourteen In applied AI, early success can be misleading. The model deploys. Metrics look strong. Stakeholders are confident. Then, quietly, performance begins to slip. Accuracy drops slightly. Latency creeps up. Edge cases surface. Confidence scores skew. Within weeks, the system that “worked perfectly” becomes difficult to trust. I call this the Second Week Drop. It’s rarely a modeling problem. It’s an operational one. Most AI systems degrade because: • Real-world data drifts from training data • Integration layers introduce messy, unseen inputs • Teams monitor uptime but not model health • Feedback loops for retraining were never engineered • Models are treated like deterministic code instead of statistical systems AI is not a one-time deployment. It is a living system. If you don’t design for drift, observability, retraining, and scale from day one, performance decay is inevitable. The real benchmark of a successful AI system isn’t launch metrics. It’s whether it’s still reliable 30 days later. For those building production AI — what has been your biggest post-deployment lesson? Read more here: interviewready.io/blog/why-d…

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…

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Why your AI model still needs a "personality" check? Ever noticed how a fine-tuned model can be technically perfect but still totally miss the mark on tone? It’s because SFT (Supervised Fine-Tuning) only teaches a model how to talk. To teach it who it's talking to, you need Preference Tuning. We just wrote a quick breakdown of the 4 methods in very simple way from anyone to understand: 🟤DPO: The current favorite. Simple, stable, and skips the RL headache. 🟤GRPO: The logic behind DeepSeek-R1. It’s a massive win for math and coding because it's way more efficient with memory. 🟤RLHF: The "Gold Standard" for high-stakes safety, but a bit of a nightmare to train. 🟤Best-of-N: The "audition" trick for when you don't want to retrain but need better results. If you're building agents or trying to make your model sound less like a robot and more like a specialist, give this a read 🔗interviewready.io/blog/all-y…

do something @GeminiApp ,@AnthropicAI @grok and @OpenAI nothing new for few days now