Why per-company prep beats generic FAANG drills
A generic "FAANG interview prep" plan averages across companies whose loops are not actually similar. Google L4 has no system design round. Meta E4 always does. Amazon SDE2 fails coding-strong candidates on Bar Raiser if Leadership Principles don't surface. A practice loop that doesn't load the right rubric trains the wrong signals.
Each combo below practices against the documented rubric for that specific company × level. Questions are sourced from public reports — Glassdoor, IGotAnOffer, hellointerview.com, Phil Shan's Medium, LeetCode Discuss, 1Point3Acres, Zhihu interview reports. No fabricated questions. No "we polled 10,000 candidates" stats. If a question is on the page, you can click through to the source thread it came from.
How Applr's mocks differ from generic LeetCode-style
Most mock interview tools train one signal: can you produce a working solution under time pressure. That's necessary but not sufficient. The candidates who actually convert offers also signal correct trade-off reasoning, behavioral fit per company values, and ability to articulate decisions out loud — and those signals are what most candidates fail on.
Applr loads the documented rubric for the company you're targeting before each session: which behavioral framework gets scored (LP / Googliness / Move Fast / Kind-Smart-Creative / etc.), what stack signals matter (Go for Uber, PHP/Hack for Meta, C++ for HRT/Optiver, JAX for DeepMind), what specific failure modes get flagged (Bar Raiser claim drift, Apple secrecy violation, Cloudflare corporate-speak). Sessions are bilingual (EN + ZH) — useful for international students who want to first-draft in native language before delivering in English.
Practice the loop you're actually facing, against the rubric the company actually uses.
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