• The author discusses how cultural fit is incredibly important for early-stage, small startups (fewer than 10 people), but notes that some interview processes take this priority too far.
• Three years prior, the author applied for a founding engineer role at a mental health startup focused on improving therapy access for at-risk youth.
• Following an uneventful initial screening with the founder and head of engineering, the author was invited to a 90-minute "culture fit" video call with the head of engineering.
• Instead of technical evaluations, the interview consisted entirely of invasive, non-technical "trauma-baiting" questions regarding the author's biggest life challenges and hardest days.
• Encouraged by an environment presented as a "safe space," the author shared deeply personal details about family struggles and failed relationships, while the interviewer shared very little in return.
• The session left the author completely emotionally drained without ever writing or reviewing code.
• After receiving a generic rejection email 24 hours later, the author felt intense shame, anger, and embarrassment, feeling as though their core personhood—rather than their technical skills—had been judged and rejected.
• The author concludes that hiring managers and founders must evaluate cultural fit through methods that respect candidates' boundaries instead of forcing them to share deeply personal trauma to secure employment.

Hacker News Discussion

• Absurd and Unqualified Interviewers: Users shared experiences with incompetent interviewers, including an incident where a mobile developer was tasked with interviewing Machine Learning Engineers; the interviewer read off rigid ChatGPT-style questions, rapid-fired acronym tests, and repeated questions in an unfocused camera feed.
• Compliance and Ghost Interviews: Commenters noted that highly dysfunctional or overly aggressive interviews sometimes occur when a company has already chosen an internal or preferred candidate but is legally or contractually mandated to interview a public pool of applicants.
• Over-indexing on Trivia: A sub-discussion emerged around an engineer who was rejected for not instantly recalling a basic Python string method (.find()). Users debated whether failing to recall minor syntax during high-stress situations is a fair reason to disqualify candidates, noting that poor interviewers focus heavily on specific trivia while good interviewers focus on holistic engineering processes.
• Power Trips and Red Flags: Many agreed that bizarre or overly intense interview behavior functions as an immediate red flag, saving candidates the trouble of working for micromaging executives, "zombie companies" that purely cruise on VC funding, or toxic environments.

Great questions

• The Problem: Traditional technical interviews (like LeetCode or basic take-home assignments) are becoming obsolete because LLMs can solve them instantly, making it difficult to distinguish between a candidate's skill and AI assistance.
• Embracing AI: Rather than banning LLMs, the author suggests integrating them into the process to see how candidates use them as "force multipliers" rather than crutches.
• Strategy 1: Deep-Dive Project Retrospectives: Focus on a candidate's past real-world projects. LLMs struggle to simulate the specific, messy constraints and "war stories" of a unique codebase. Asking "Why was this specific trade-off made?" reveals genuine experience.
• Strategy 2: Large-Scale Codebase Navigation: Provide a complex, "messy" existing codebase and ask the candidate to explain its inner workings. Allow LLM usage but observe if the candidate blindly trusts the AI or uses their own intuition to spot hallucinations and architectural flaws.
• Strategy 3: AI-Assisted Code Review: Give the candidate code with subtle logical errors that an LLM might miss or incorrectly justify. Evaluate whether the candidate can critique the AI's suggestions and apply contextual judgment.
• Evaluation Criteria:
  • Technical Judgment: Does the candidate treat LLM output as a starting point or an absolute truth?
  • Problem Decomposition: Can they break complex tasks into smaller, specific prompts?
  • Validation: Do they cross-check AI answers against documentation or their own mental model?
• Core Philosophy: AI doesn't create great developers; it amplifies them. The goal is to find "amplified" seniors, not "automated" juniors.

https://www.instagram.com/nprfreshair/reel/DNVlf2tMstg/?hl=en

Terry Gross reading a book has rendered it useless for others to read.

Dog earing of top corner for interesting sections or questions she may have for the author.

Dog earing bottom corner as an indicator of remembering facts for the intro or for sentences she wants to quote.

Uses front of book for connecting themes and focus, so she won't forget it.

Introductions/prologues for quick overviews of what the book is about and why they wrote it.

https://www.instagram.com/reel/DNYh6b_uBwd/?hl=en

Terry Gross reads slowly to start and speeds up as she continues. She annotates and dog-ears as she reads and then makes notes and questions after she's done.

• Finding Wins Above Replacement: Look for candidates who made a significant impact in their past roles, distinguishing their actions from team efforts. This highlights their talent, agency, and individuality.

• Key Traits to Identify:

  • Creativity: Ability to identify bigger problems and opportunities rather than merely following orders.
  • Resourcefulness & Follow-through: Persistence to complete tasks even when facing obstacles.
  • Vision: A thoughtful, original future vision with coherent life choices.

• Effective Questions to Ask:

  • "Tell me about your best [accomplishment/task]."
  • "If your life was a book, give me the chapter titles from birth till now."

• Chip on the Shoulder: Candidates with a drive to prove themselves can be valuable. Look for those with optimism and resilience rather than deep insecurities or negativity.

• Challenges as Motivation: Present company challenges and assess if candidates are excited by the opportunity to make an impact rather than deterred by imperfection.

• High EQ & Persuasion: Evaluate their ability to adapt communication and influence effectively, especially in group interactions.

• Theory for Excellence: Past excellence in any field (sports, arts, academics) suggests transferable drive and discipline for unrelated roles.

• Understanding Competence: Candidates should know their strengths, weaknesses, and preferred tools or methods.

• Spike Potential: Tailor questions to their profile and assess specific strengths that hint at future excellence.

• Coachability & Openness: Look for candidates open to feedback, willing to improve, and capable of committing even when they disagree.

• Unpretentiousness: Seek candidates who are self-aware, lighthearted, and pleasant to work with.

• Assessment Methods:

  • Use scenario-based interviews, group exercises, or role-playing to evaluate real-time reactions.
  • Gather backchannel references for additional insights.

that sounds like a useful analogy. Prompting like you are the algo's improv partner. The flipside seems to be the impact the author himself is after: being prompted along new lines of inquiry, making the script your improv partner in return.

1. "Tell me about the last time you refactored a module or class."
2. "Tell me about the last piece of user feedback that became a feature."
3. "Tell me about the last feature of yours that got dropped."

Looks like a wonderful time to be a developer... not sure if this is true for everybody, but interesting for sure.

Schmid, P., & Lewandowsky, S. (n.d.). Tackling COVID disinformation with empathy and conversation. The Conversation. Retrieved December 15, 2021, from http://theconversation.com/tackling-covid-disinformation-with-empathy-and-conversation-173013

valuable HR tip

This is a good "easy question" that was mentioned in the readings. I see how an easily answered question is good to use, because the interviewee is able to give a lot of detail and will normally get into the conversation more because they know that they have a lot to say on the topic. It opens up the talk and eases the flow, I think.

This reveals that Savannah's interview with her mom was motivated by the desire to know more about what happened to both of them and what her mother felt about the circumstances. The first 4 questions definitely felt planned. I think that Savannah wrote them down beforehand, so that she could get specific answers. This shows good planning.