WebinarNotes/raw/sources/Yulia interview.md

Executive summary

An HR/recruiting lead (SPEAKER_00) brings a senior programmer/AI consultant (SPEAKER_01) three team pain points, and he demonstrates that all of them are solvable today with Claude plus reusable "skills." The firm conclusion: their two recruiting problems collapse into one — build a searchable candidate knowledge base (record interviews → auto-transcribe → auto-generate profiles) — and the next step is a webinar he'll plan over the coming weekend, with a possible paid build of an HR tool to follow.

Who's who

• SPEAKER_00 → HR/recruiting lead (female), collecting her team's AI pain points and organizing the webinar. Self-described beginner; refers to Nina, Sergiy and "Andryuk" as the people who approve budget, and to "Jean-Luc/Zhenlyuk" who posts about AI.
• SPEAKER_01 → Senior programmer (~20 years), works on "Inspectron" and its clients. Heavy Claude user on the $200 max plan; proposes the webinar and offers to build the HR system for money.

Key themes

AI usage has distinct levels, and most people are stuck at level one. He maps a ladder: web chatbot → built-in memory → Claude Code/Cowork with local file access → CLAUDE.md → skills → Obsidian knowledge base → RAG. The practical ceiling for non-programmers is CLAUDE.md plus skills.

"Usually you need CLAUDE.md and skills. That's your maximum."

Skills are reusable process memory — solve first, skill-ify after. The recurring mistake is building the skill up front. Instead, solve a task with Claude once, then ask it to package that into a skill it can rerun on command.

"The mistake people always make is they create a skill first and then try to shove it somewhere… You first solve a task with Claude; the moment you reach the final solution, you say — now create a skill from this."

Both recruiting problems are the same problem: a candidate base plus search over it. Recording interviews, auto-transcribing (with speaker diarization), and generating standardized profiles feeds a knowledge base you can later query by skill — including finding people you already spoke to who match a niche stack.

"You need a candidate database and you need search over that candidate database."

AI automates all information work; the human keeps the human parts. His central thesis for the webinar — the residual human role is connection and communication, not information processing.

"The human's role in this process is just to be human — introductions, communication… Because our reality is all information work. And AI can replace all of it."

AI prices will rise, not crash — so invest in the skill now. He argues the "bubble" pops upward: capable AI gets more expensive, and the leverage shifts to knowing which models to use.

"What I now buy for 200 will cost about 1,000… Expensive AI will become even more expensive."

Economics favor renting GPU over owning. Live demos (RunPod for transcription, Claude driving a browser to pull LinkedIn HR contacts into a Markdown list) frame the cost: ~$0.10 per candidate to rent, versus a $1,500–2,000 machine — though a home GPU can transcribe overnight for free at roughly 2× real-time on CPU.

"It's not per hour of video. The server costs about a dollar an hour, but you only need it for ~6 minutes — so 10 cents."

Conclusions

• The next webinar will be an overview of the levels of AI usage and how to use them; SPEAKER_01 will draft a plan Saturday–Sunday, present it on a call, and there is no deadline — SPEAKER_00 was explicit about zero time pressure.
• The team's two recruiting problems are "banal" and easily solvable: a candidate knowledge base plus semantic search, fed by recorded-interview transcripts and standardized profiles.
• Recommended stack: Claude (Claude Cowork for non-programmers), with CLAUDE.md + skills as the realistic ceiling; RAG only matters at corporate scale. He prefers Claude over GPT/Google for professional work; cheaper models (e.g. Qwen) do the same tasks "just worse."
• The immediate good-practice change regardless of tooling: start recording candidate interviews now, one file per candidate, named by candidate.
• Renting GPU (~$0.10/candidate) is cheaper than buying hardware, but a home GPU works overnight for free if you already have one.
• A paid build of the HR system is on the table — priced per project, roughly "$5 to $100," with more scoping needed; possibly delivered as a shared monthly service on his subscription.

Takeaways

• Solve a task with Claude first; only then have it create a skill from the finished workflow. Don't build skills speculatively.
• Turn any correction loop longer than ~3 messages into a skill.
• Use CLAUDE.md for persistent per-project instructions, and add "always keep CLAUDE.md up to date" to make it self-maintaining memory.
• Constrain prompts — your job as a user is to "narrow the variability of interpretation." Vague asks ("make me a dashboard") invite unwanted "fantasy."
• Learn from docs.claude.com; there's no single good course yet. SPEAKER_01 will cover this in the webinar.
• Memorable framing to reuse: "Split yourself into two people — one slightly smarter than you, your manager; one slightly dumber, your subordinate. You become the smarter one; the AI is the dumber one."
• Company-scale idea worth flagging to leadership: a lightweight AI "assistant" that chats with employees and forms an information core — automating most of the PM/status-reporting role.

Open questions

• Exact scope, deliverables, and price of the HR system — SPEAKER_01 needs more task definition before quoting.
• Webinar date and title — SPEAKER_01 to propose; possibly with pre-recorded segments so nobody waits on live runs.
• Rent GPU vs. buy a machine with a GPU — left as "something to think about," possibly folded into SPEAKER_00's next hardware purchase.
• Larisa's Claude memory complaint — fixable (built-in memory, "remember this," CLAUDE.md), but her specific BA/PM task was never detailed.
• Can it pull a recruiter's full LinkedIn history at scale? No — LinkedIn bans bots; only a shallow pass (~20 contacts at a time) is safe.