SWE Interview Essentials
- Aman Kumar posted 15 core system‑design topics to master for Big Tech interviews, like consistent hashing and CAP theorem. (x.com) - Sukh Sroay recommends focusing on 14 LeetCode problem patterns and links to an 11.4K‑star GitHub repo. (x.com) - Aakash Gupta breaks down AI system‑design interviews into model selection, orchestration and evaluation, favouring detailed agent flows. (x.com)
Software engineering interview prep is getting packaged into three repeatable tracks: system design, coding patterns and artificial intelligence system design. (github.com, github.com, aakashg.com) System design interviews test how a candidate would build large services: how requests get routed, where data is stored, what breaks under load and which tradeoffs they choose. Common topics in current prep guides include load balancing, caching, sharding, replication, consistency models and the CAP theorem, which frames tradeoffs during network failures. (designgurus.io, github.com) Coding interviews are getting taught as pattern recognition instead of long random problem sets. Sean Prashad’s LeetCode Patterns repository groups questions by recurring structures and says the goal is to practice “common patterns rather than randomly tackling questions.” (github.com, github.com) That repository had 12.5 thousand GitHub stars on its main page when viewed on April 20, 2026, and a GitHub Topics page listed it at 11.8 thousand. A README view tied to the same project showed 11.4 thousand stars, which is the figure circulating in interview-prep posts. (github.com, github.com, github.com) Artificial intelligence system design interviews add a third layer: picking a model, wiring prompts and tools together, and deciding how success will be measured. In a mock interview published April 17, 2026, Aakash Gupta and Ashutosh Singh framed the exercise around designing a churn-reduction agent and said interviewers want candidates to “go deep on a technical topic.” (aakashg.com) That shift changes what “design” means in an interview. A candidate now may need to explain not just databases and queues, but also how an agent gets user signals, calls tools, handles failures and gets evaluated against a business goal such as reducing churn. (aakashg.com, designgurus.io) The system-design side still rests on old distributed-systems ideas. A widely used open-source guide with 36.4 thousand GitHub stars lists consistent hashing, CAP theorem, rate limiting, service discovery and consensus algorithms among the core concepts for interview prep. (github.com) The coding side stays narrower. The LeetCode Patterns project recommends first knowing the runtimes and operations for arrays, maps, linked lists, queues, heaps, stacks, trees and graphs, plus breadth-first search, depth-first search, binary search and recursion. (github.com, github.com) Taken together, the current prep playbook is less about memorizing hundreds of isolated questions and more about mastering a smaller set of reusable ideas. The candidate who can explain a cache, spot a sliding-window problem and sketch an agent workflow is now training for three interviews at once. (github.com, github.com, aakashg.com)
