Software Engineer Interview Prep Podcast

SEASON 1 — "The Shift"Listen to - S1E1 Interview an engineer who transitioned from backend → AI engineer. Their story IS the season premise.

When I run kubectl apply, the request is sent to the Kubernetes API Server, which acts as the entry point to the cluster.The API Server processes the request through several stages:Authentication – validates the client (certificates, tokens, etc.)Authorization – checks permissions using RBACAdmission ControllersMutating (e.g., inject defaults, sidecars)Validating (ensure request is compliant)Once validated, the object is persisted.The API Server stores the Deployment object in etcd, which is the cluster’s consistent key-value store.At this point, the desired state is recorded—but nothing is running yet.The Kubernetes Controller Manager detects the new Deployment via the API Server’s watch mechanism.Deployment Controller creates a ReplicaSetReplicaSet Controller creates the required PodsThis is all driven by control loops comparing:Desired state (in etcd)Current state (actual cluster)The Pods are created without a node assigned.The kube-scheduler:Filters nodes (resource constraints, taints, node selectors)Scores remaining nodes (resource availability, affinity rules)Assigns the best nodeOnce scheduled, the kubelet on the node pulls the image and starts the container."The important thing is Kubernetes is entirely declarative and event-driven.Nothing is executed immediately—instead, components continuously reconcile actual state toward desired state."

The Sliding Window Algorithm is a powerful technique used to reduce the time complexity of problems involving arrays or strings—specifically those that require finding a sub-segment that meets certain criteria.Instead of using nested loops O(n^2), the sliding window maintains a dynamic range that "slides" across the data, usually bringing the complexity down to O(n).Problem:Find the maximum sum of a contiguous subarray of size `k`.public class SlidingWindow { public static int findMaxSum(int[] arr, int k) { int n = arr.length; if (n < k) return -1; int windowSum = 0; // 1. Compute sum of the first window for (int i = 0; i < k; i++) { windowSum += arr[i]; }int maxSum = windowSum;// 2. Slide the window from index k to n-1 for (int i = k; i < n; i++) { // Add the next element, remove the first element of the previous window windowSum += arr[i] - arr[i - k]; maxSum = Math.max(maxSum, windowSum); } return maxSum; }}

Video overview of Distributed Rate Limiter

Video Summary of - [DSA] Data Structure and Algorithm (DSA) problem-solving strategies and patterns