Video summary

How I Switched to Abnormal AI | Interview Rounds, Resume Mistakes & Tips

Main summary

Key takeaways

News and Commentary

Summary of Main Points

  • AI’s impact on hiring and work: Hiring appears to have slowed compared to earlier periods (especially after COVID-heavy hiring). The creator doesn’t claim AI is definitively the cause, but they believe AI may contribute. They note that AI makes development faster (tasks that took a week can be done in days), but shifts the work from writing more code to reviewing, understanding, and testing code more carefully.

  • Typical interview process (varies by company):

    1. Telephonic/HR round: Covers current compensation, motivations for switching, and related background questions.
    2. Coding/DSA round (DSA or LLD depending on company):
      • Often includes problems that resemble LeetCode-style medium/hard-medium.
      • Example topics mentioned: graphs, DFS, backtracking.
      • Advice: If you understand core techniques (DFS/BFS/backtracking and basic graph concepts), DSA becomes manageable.
    3. LLD/design principles round:
      • Focuses on applying SOLID principles and design patterns through your code quality.
      • Interviewers may not ask “what is SOLID/pattern X,” but they test whether your implementation reflects them.
    4. Take-home + debrief (mentioned as a process some companies use).
    5. HM (Hiring Manager) round / behavioral:
      • The creator finds this harder than coding rounds.
      • Common behavioral prompts: explain your project, technical challenges, organizational challenges, and sometimes time allocation (e.g., how you divide time across a day).
      • Advice: Be prepared to answer spontaneously, since interview conditions differ from normal work mode.
    6. AI-based rounds: Some interviews may incorporate AI, but they stress you must still be able to complete at least one round without AI assistance.

  • How referrals and aggressive applying matter:

    • The creator believes overall job response rates are lower post-AI.
    • They personally didn’t use referrals and still got replies “touch wood,” but when they tried referrals later, they didn’t receive responses—so they can’t confirm referrals reliably helped them.
    • They suggest aggressive application may help more than relying on referrals, while still noting that referrals don’t guarantee acceptance.

  • Practical preparation tactics (tools/strategy):

    • Using an AI subscription/chat tool (referred to as “Clot/Clout”) in mentor mode:
      • The creator asks what to expect in interview rounds and practices with it.
    • Keeping a compiled notebook/pile of past interview questions:
      • They write down questions asked in interviews, draft answers, and rely on the idea that interview questions often repeat.
    • For interviews: they emphasize cross-validating with the AI/mentor while still writing answers in their own words and testing concepts.

  • Resume tips and ATS considerations:

    • Use a clean template (they recommend LaTeX/Overleaf vs. other tools that may break links).
    • Keep the most relevant sections near the top:
      • If you have experience, put experience above projects.
    • Make the resume ATS-friendly since many companies scan through ATS before HR reviews it.
      • Read the JD and ensure key skills/phrases match what ATS is likely to parse.
      • Use free resume-scoring/analysis tools and upload the resume to see weaknesses.

  • Interview coverage recap:

    • The creator summarizes that the key areas are DSA, LLD, system design (if experienced), take-homes, HM/behavioral, and resume readiness.
    • They reiterate that AI is helpful for learning, but interviewers still expect you to validate and understand the material yourself.

Presenters / Contributors

  • Shia Sa (the creator/presenter)

Original video