31 lines
1.7 KiB
Markdown
31 lines
1.7 KiB
Markdown
# Nina
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#entity
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## Summary
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HR recruiter at [[virtido]]; among the most AI-forward recruiters on her team. Interviewed by [[eugene]] as a webinar-audience proxy — she supplies the concrete HR use cases (job descriptions, interview write-ups, candidate sourcing) that validate the webinar thesis. Also an ultra-trail runner (50+ km).
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## Current Understanding
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Nina is the enthusiastic practical adopter: uses paid ChatGPT, LinkedIn Recruiter, and Manatal (ATS). Her biggest pain is interview write-ups — note-taking during a call wrecks the conversation, so automatic transcription is the part that actually changes her work ("this transcript is honestly the most important thing"). She estimates going from ~5 to ~25 candidates/day with Eugene's tooling, but values the **cognitive-load reduction** more than the throughput. Her read on colleagues: they aren't resistant, they lack a frictionless path — "they'd use it if it just transcribed everything for them."
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## Evidence
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- Pain points, transcript-over-summary, friction-not-resistance, multiplier framing, 5→25 estimate — [[2026-07-14-nina-interview]].
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- Referenced as a budget-approver contact in [[2026-07-14-yulia-interview]].
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## Related Pages
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- Entities: [[eugene]] (interviewer), [[yulia]] (team colleague/lead), [[virtido]]
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- Concepts: [[solve-first-then-skillify]], [[skills-as-memory]], [[levels-of-ai-usage]]
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## Contradictions / Uncertainty
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- None recorded; her account is first-hand and consistent with Yulia's.
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## Next Questions
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- Would her team actually adopt the transcribe→summarize tool if delivered — and can it integrate with Manatal?
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- Multilingual output (EN/DE/UK) was flagged as an unlock — which languages dominate her candidate pool?
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