## 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.
