Yulia Interview — Levels of AI Usage & the Candidate Knowledge Base

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Source Metadata

Raw path: raw/sources/Yulia interview.md
Source type: interview conclusions/insights doc (auto-generated summary; speakers unnamed in text)
Participants: yulia (SPEAKER_00, HR/recruiting lead — name inferred from filename, Status: tentative) and eugene (SPEAKER_01, senior programmer ~20 years, works on inspectron, $200 Claude max plan)
Ingestion date: 2026-07-14

AI usage has distinct levels, and most people are stuck at level one: web chatbot → built-in memory → Claude Code/Cowork with local files → CLAUDE.md → skills → Obsidian knowledge base → RAG. The practical ceiling for non-programmers is CLAUDE.md + skills (levels-of-ai-usage).
Solve first, skill-ify after — the recurring mistake is building the skill up front; instead reach the final solution once with Claude, then say "now create a skill from this" (solve-first-then-skillify). Turn any correction loop longer than ~3 messages into a skill.
Both recruiting problems are the same problem: a candidate database plus search over it — record interviews, auto-transcribe with diarization, generate standardized profiles, then query by skill.
AI automates all information work; the human keeps the human parts — "the human's role in this process is just to be human — introductions, communication." (Converges with connections-as-moat.)
AI prices will rise, not crash — "what I now buy for 200 will cost about 1,000"; the bubble pops upward, and leverage shifts to knowing which models to use. Status: tentative (prediction).
Renting GPU beats owning for this workload: ~$0.10/candidate on RunPod vs a $1,500–2,000 machine — though a home GPU transcribes overnight for free.

Live demos: RunPod transcription; Claude driving a browser to pull LinkedIn HR contacts into a Markdown list. LinkedIn bans bots — only shallow passes (~20 contacts) are safe.
Immediate no-tooling practice change: start recording candidate interviews now, one file per candidate, named by candidate (consent implied per 2026-07-14-nina-interview).
Stack recommendation: Claude (Cowork for non-programmers); RAG only matters at corporate scale; cheaper models (e.g. Qwen) do the same tasks "just worse."
Prompting principle: your job is to "narrow the variability of interpretation" — vague asks invite "fantasy."
Reusable framing: "Split yourself into two people — one slightly smarter (you, the manager), one slightly dumber (the AI, your subordinate)."
Deal context: webinar plan to be drafted over a weekend, no deadline; a paid build of the HR system is on the table (rough "$5 to $100" per-project range needs scoping), possibly a shared monthly service on Eugene's subscription.
Company-scale idea: a lightweight AI "assistant" chatting with employees to form an information core — automating much of the PM/status-reporting role.

Exact scope, deliverables, and price of the HR system build.
Webinar date/title (later fixed as "From a chat box to your own operating system" per 2026-07-14-nina-interview — this interview likely predates it).
Rent GPU vs. buy a GPU machine — folded into Yulia's next hardware purchase decision?
Larisa's Claude memory complaint — fixable via built-in memory/CLAUDE.md, but her actual BA/PM task was never detailed.
Is SPEAKER_00 really named Yulia, and is she at virtido? Inferred from filename + references to Nina/Sergiy. Status: tentative.

Created yulia; created concepts levels-of-ai-usage and solve-first-then-skillify.
Updated eugene (Inspectron, $200 plan, paid-build offer), connections-as-moat (Eugene independently lands on the human-connection residual), skills-as-memory (~3-message correction-loop heuristic), overview.