Listen and watch now on YouTube, Spotify,
• Antithesis – If you’re using agents to code, the problem isn’t writing the code but making sure it didn’t break anything. Antithesis runs your whole system in a hostile environment and identifies hard-to-find bugs before users hit them in production. With Antithesis, teams like Jane Street, Fly.io, and the etcd community use agents safely and ship better code, faster.
• Sentry – application monitoring software built by developers, for developers. Sentry’s
• Google Cloud Run – a fully managed platform that runs your code directly on top of Google’s scalable infrastructure. Run frontend and backend services, batch jobs, host LLMs, and agents without managing infrastructure. Oh, it has automatic zonal failover out of the box – something that Coinbase
Navdeep Singh – oftentimes better known as NeetCode – is the creator of NeetCode.io, one of the most popular coding interview preparation platforms and YouTube channels for software engineers. Before building NeetCode full-time, he worked as a software engineer at Amazon and Google.
In this episode of The Pragmatic Engineer, I sit down with Neet to discuss his path from Amazon and Google to building his own startup, why he left Amazon after just two months, what he learned at Google, and the decision to leave a stable engineering career to bet on himself. We also discuss what coding interview preparation teaches beyond passing interviews, the value of going deep on difficult problems, and why systems thinking and domain expertise remain essential engineering skills in the age of AI.
Throughout the conversation, NeetCode makes the case that learning hard things is one of the *single best *investments an engineer can make, helping build the judgment and expertise that remain valuable no matter how the tools change.
Here are 10 interesting takeaways from our chat:
1. Companies have no real method for evaluating engineers – and likely never did. Neet believes the leetcode-style interview process has persisted because it scales well at large tech companies that need to train hundreds or thousands of interviewers, not because it predicts job performance well.
2. The CAP theorem’s “two-out-of-three” framing is widely taught, but technically shaky. Neet believes this theory of distributed data systems is incomplete, and says he felt validated when researcher and author Martin Kleppmann criticized it. It’s a reminder to think independently and not accept theories without understanding them.
3. Amazon’s intense culture left Neet reluctant to ask questions – which paradoxically, helped at Google. In Neet’s first job, he got used to working alone and not seeking help when needed, and continued this working style at Google. His manager there interpreted that behavior as independence, and as a result, he won rapid promotion from L3 to L4 (mid-level engineering role).
**4. The NeetCode YouTube channel took off after he said he’d have to post less. **Before viewers knew Neet had got a software engineering job at Google, his audience was small. But it turned out that announcing he’d have to post less for this reason boosted his channel! Suddenly, lots of people wanted to know how he’d landed the role.
**5. Cheating tools are helping to resurrect in-person, whiteboard interviews at Google. **Neet notes Google has restarted onsite coding interviews because it’s the only way interviewers can be sure that candidates aren’t using AI-powered cheating tools which make data structure and algorithms (DSA) interviews easy to pass.
**6. Neet finds AI most valuable as a tech debt and refactoring assistant. **He’s using AI to clean up years’ worth of low-quality code on NeetCode’s backend, which also validates the decision to take shortcuts in the knowledge they can be corrected later.
7. ‘Effort’ is becoming the differentiator as AI makes everything else cheap. Neet says how you can prompt almost anything, but the capacity to be engaged with and care about your work, and to defend decisions you make, cannot be prompted by an AI tool. These depend on personal qualities like effort and dedication.
**8. Announcements of the death of coding are exaggerated. **Despite dramatic improvements in the performance of AI models, Neet does not foresee the majority of engineers being laid off. In fact, he sees the opposite: devs are busier than ever.
**9. Humans are likely to remain better at weighing up tradeoffs than LLMs are. **It’s a fact that LLMs have become a lot better at coding, but Neet doubts they will be much help in decisions involving judgments about tradeoffs.
**10. When hiring for NeetCode, personality traits and motivation matter more than coding skill. **Neet’s best recent hire is still an undergrad with little coding experience, but does exceptionally well thanks to possessing high agency. Neet says: “even if they have no idea how to start it, by a week later, they’ll have learned everything about it.”
• Learnings from conducting ~1,000 interviews at Amazon
• How experienced engineers get unstuck in coding interviews
• The Reality of Tech Interviews in 2025
• Tech hiring: is this an inflection point?
• AI fakers exposed in tech dev recruitment: postmortem
▶00:00 Intro
▶02:57 Neet’s take on coding interviews
▶06:41 Getting into tech
▶08:56 Why Neet isn’t a fan of the CAP theorem
▶13:12 Quitting Amazon after two months
▶18:22 Google vs Amazon
▶22:26 The origins of NeetCode
▶25:27 Leaving Google to go all in on NeetCode
▶32:02 Why Neet doesn’t fix every bug
▶39:26 The value of coding interview prep
▶42:57 Systems thinking and domain expertise
▶47:28 Hiring at Big Tech
▶52:15 Tech stack at Neetcode
▶57:57 The NeetCode redesign contest
▶1:01:46 The future of software engineers
▶1:09:04 Hot takes: AGI, AI skill erosion, personality traits
▶1:22:49 “Maybe some people should just give up”
▶1:24:39 How to be a standout engineer
▶1:27:55 Book recommendation
Where to find Navdeep Singh (NeetCode):
• LinkedIn: https://www.linkedin.com/in/navdeep-singh-3aaa14161
• YouTube: https://www.youtube.com/c/neetcode
• Website: https://neetcode.io
Mentions during the episode:
• A critique of the CAP theorem: https://martin.kleppmann.com/2015/09/17/critique-of-the-cap-theorem.html
• Designing Data-intensive Applications with Martin Kleppmann: https://newsletter.pragmaticengineer.com/p/designing-data-intensive-applications
• PACELC design principle: https://en.wikipedia.org/wiki/PACELC_design_principle
• Amazon Chime: https://aws.amazon.com/chime/getting-started
• Musk’s 5 Step Design Process: https://modelthinkers.com/mental-model/musks-5-step-design-process
• AI Engineering with Chip Huyen: https://newsletter.pragmaticengineer.com/p/ai-engineering-with-chip-huyen
• Angular: https://angular.dev
• Firebase: https://firebase.google.com
• TypeScript: https://www.typescriptlang.org
• An update on recent Claude Code quality reports: https://www.anthropic.com/engineering/april-23-postmortem
• Building Claude Code with Boris Cherny: https://newsletter.pragmaticengineer.com/p/building-claude-code-with-boris-cherny
• Sora: https://en.wikipedia.org/wiki/Sora_(text-to-video_model)
• Attention is all you need: https://arxiv.org/abs/1706.03762
• The End of Programming as We Know It: https://www.oreilly.com/radar/the-end-of-programming-as-we-know-it
• Satya Nadella on X: https://x.com/satyanadella
• Replit: https://replit.com
• Lovable: https://lovable.dev
• 37signals: https://37signals.com
• DHH’s new way of writing code: https://newsletter.pragmaticengineer.com/p/dhhs-new-way-of-writing-code
• MongoDB: https://www.mongodb.com
• Maybe some people should just give up:
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