Build projects, not just DSA
A recent thread argues that in 2026 candidates should prioritize three real projects, AI‑tool fluency, basic system design and open‑source contributions over only grinding DSA — the pitch being visible proof of impact beats pure problem sets. The post frames this as a strategic route into FAANG‑level roles from non‑top‑tier backgrounds. (x.com)
Multiple hiring guides now recommend 3–5 deployed, documented projects (live URL, README, architecture notes) as portfolio essentials that recruiters use to judge practical impact. (techtimes.com) Anthropic’s AI Fluency Index measured 11 observable behaviors across 9,830 Claude.ai conversations to quantify “AI fluency” in users during January 2026. (anthropic.com) Microsoft’s AI fluency learning path and companion materials explicitly include Copilot training and bite‑sized modules for practical AI tool use. (learn.microsoft.com) Google’s system‑design interviews typically run about 45 minutes and often include 1–3 design rounds for mid‑level roles (L4/L5+) as a core evaluation step. (igotanoffer.com) Meta and Amazon likewise use 45‑minute design interviews for L4+/mid‑to‑senior hires, making large‑scale architecture reasoning a standard filter in FAANG loops. (igotanoffer.com) Current prep guides list DSA pattern mastery (Two‑Pointers, Sliding Window, DFS/BFS, DP, Union‑Find) as the durable foundation that underpins interview problem solving. (educative.io) At the same time multiple analyst posts warn that mass LeetCode grinding (e.g., “do 500 problems”) without system‑design practice, mock interviews, or demonstrable projects is an incomplete strategy. (leetcopilot.dev) Cloud vendors and platform docs highlight open‑source toolchains (TensorFlow, PyTorch, Hugging Face, model infra) as production accelerators, which explains why public contributions become visible proof of engineering impact. (cloud.google.com) Industry roundups pointing to fast‑moving OSS projects and high‑visibility repos show maintainership and PRs are increasingly treated as hiring signals. (analyticsinsight.net) Practical prep stacks cited across 2025–26 guides combine targeted DSA sets (NeetCode/Blind‑75), pattern courses (AlgoMonster, Educative’s LeetCode patterns), and mock interviews for coding fluency. (github.com) Parallel project playbooks — InterviewQuery’s curated AI projects and Scaler’s generative‑AI portfolio recommendations — lay out deployable stacks (RAG, LLM integration, deployment demos) that hiring teams now ask to see. (interviewquery.com)
