Abhishek Prasad

The FAANG → startup gap is widening. When I was at Uber, we could still reliably hire great engineers out of FAANG. The velocity difference existed, but it was bridgeable. Now? For a seed or Series A team, the ramp is often too steep: ambiguity everywhere no guardrails shipping is the strategy “ownership” means end-to-end, not a slice Here’s what I think happens next as big tech layoffs continue: If you’re hiring: expect more noise. A larger pool of “available” talent won’t automatically translate to startup-ready talent. Your filter gets harder: slope > pedigree. If you’re a FAANG engineer: if you want risk, speed, and real ownership, jump earlier. After ~3+ years in a highly structured environment, the pivot gets meaningfully harder. Not because you’re not capable, but because the operating system is different. And I’m seeing a pattern: A lot of FAANG engineers want to move faster… But many early-stage startups aren’t prioritizing candidates who’ve spent 5+ years in big tech. Not a knock. Just a market reality. Curious if others are seeing the same thing: What’s your “threshold” where the FAANG → seed jump gets tough? 2 years? 4 years? More?

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Give a system design interview at Google, and you will probably be asked to design something for 100M+ users. Sit in a system design round at AWS, and you will see the same scale in almost every question. System design interviews often look nothing like your real job. In the interview, you might design “Twitter at 100M QPS” but your day-to-day work might be: • Owning a couple of internal services that serve a few thousand requests per minute • Making one database and one cache behave nicely for a single region • Cleaning up flaky cron jobs, queues, and timeouts so customers stop seeing random failures So why is the interview so different from reality? • Big tech needs one bar for candidates from hundreds of teams, so they pick generic, large-scale prompts. • Extreme numbers force you to talk about trade-offs, bottlenecks, and failure modes clearly. • In 45 minutes, they cannot watch you work for months, so they simulate pressure, ambiguity, and growth. • Future proofing. You might join a “small” system today, but they want to know you can grow into bigger, more critical ones. So, how should you prepare smartly, without getting lost in the fantasy scale? This is exactly the problem Layrs solves, practice system design and scale your learning: layrs.me/practice When you’re learning by solving system design problem, keep this in mind: 1. Learn the primitives, not the brands Caching, databases, queues, load balancers, sharding, replication, rate limiting. Tools change, ideas stay. 2. Start at a realistic scale, then dial it up First design for 1M users, then let the interviewer increase it and explain what breaks and what you would change. 3. Use a simple structure every time Clarify requirements → pick data model → basic API → high level diagram → scaling → reliability and failure modes → trade-offs and extensions. 4. Talk through trade-offs.  Ex: “I am choosing X because it keeps writes simple, even though reads are slightly slower. For this product, write safety matters more.” 5. Practice with small projects Build a tiny URL shortener, feed system, or job queue yourself. You remember designs much better when you have actually fought their bugs.

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Companies like Stripe, Google, Uber, Atlassian, and PayPal keep asking these Frontend Interview Questions for high-paying roles (₹30–60 LPA): 1. Walk me through a recent frontend project you worked on and your impact. 2. How do you approach building a new frontend feature end-to-end? 3. Tell me about a complex UI you owned and the architectural decisions you made. 4. How do you handle ambiguous or unclear product requirements? 5. How would you improve our product’s frontend performance? 6. How do you define and track success metrics for a frontend change? 7. How do you design accessible and inclusive user interfaces for global users? 8. Tell me about a time you influenced product or engineering decisions with data. 9. How do you balance user experience quality with delivery speed? 10. What is your collaboration strategy with PMs and designers on cross-functional features? 11. Explain the browser rendering pipeline and where performance bottlenecks usually occur. 12. What happens in the event loop when you use setTimeout, promises, and async/await? 13. How does React’s reconciliation work and what triggers re-renders? 14. When would you use useMemo, useCallback, and React.memo, and when would you not? 15. Explain how you would implement virtualized/infinite scrolling from scratch. 16. How do you debug and fix memory leaks in a React application? 17. What strategies do you use to reduce bundle size in production builds? 18. How would you design a scalable component library for a large team? 19. Explain hydration in SSR apps and common pitfalls. 20. How do you handle race conditions in frontend data fetching? 21. What causes layout thrashing and how do you prevent it? 22. How would you implement a debounce and throttle function? 23. How do you ensure frontend security (XSS, CSRF, token handling)? 24. How would you architect state management for a large React application? 25. What are the tradeoffs between CSR, SSR, SSG, and streaming?

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