build_unlabeled_corpus.py pulls the 34,030 raw-source dialogues that
never got speech_act/slot labels (found via the earlier QA
cross-check) into their own unlabeled.jsonl, forward-filling the
per-dialogue metadata that AI-Hub's CSV export only writes on each
dialogue's first row. Verified against the actual data: 176,605
raw - 142,575 labeled = 34,030, matches exactly.
Uses google-genai + GEMINI_API_KEY instead of the Anthropic SDK, per
product decision to run the server-fallback LLM on Gemini. Same
prompt contract and escalation behavior; only the client/env var
name changed. README documents where to actually set the key.
generate_draft.py takes a few style-exemplar messages plus recent
conversation context and drafts a reply via LLM call (the server
fallback path from tech-design.md §2), with escalation baked into
the system prompt for money/appointment/emotional content. Verified
prompt construction against a real corpus dialogue and hand-compared
a generated draft to the withheld real reply (README "샘플 검증") --
no API key in this session, so the live call itself is untested.
Consolidates the AI-Hub "한국어 SNS 멀티턴 대화" TL/VL zip parts into
clean train/val JSONL (142,575 dialogues), with a QA cross-check
against the raw TS/VS source. Script and docs only -- the dataset
itself stays out of git per .gitignore, both for size and because
AI-Hub's terms restrict redistribution.
Consolidates what's ready (doc set, PoC plans/materials, prototype)
vs. what still needs a human to execute (recruiting, interviews,
Go/No-Go calls), plus a Q1-Q7 confirm/revisit checklist for the
actual planning meeting.
poc-materials.md only tests how the recipient reacts to the twin; Q3
asks whether the person delegating to it actually wants to. Adds a
separate screening + interview script for that side, with guidance
on sequencing it before the peer role-play so the two perspectives'
gap becomes a signal in itself.
Recruiting message and data-consent blurb for PoC #1, a role-play
script for the one scenario the click prototype doesn't cover
(emotional escalation), and a shared post-session interview guide —
so participant recruitment can start without drafting these from
scratch.
Built an interactive click-through prototype covering the read-receipt,
identity-confirmation/veto, and escalation scenarios plus the autonomy
settings screen. Link it into PLANNING.md's checklist and poc-plan.md
so PoC #3 role-play can reuse it as stimulus material instead of
building scenario scripts from scratch.
Turns the two highest-priority open risks (on-device tone realism,
impersonation/trust acceptance) into runnable protocols: sample
collection, blind evaluation, role-play scripts, and pass/fail
thresholds, so results can update decision-log.md and risk-log.md.
Update PLANNING.md §2 from empty checkboxes to the working answers in
decision-log, fix the reverse OS/self-app wording, and clarify roadmap
Phase 3 so OS-layer growth does not rewrite the start order.
Co-authored-by: okuma <o0kuma@users.noreply.github.com>
Introduce a single source of truth for agent guidance during the
planning-docs phase: document authority, tentative Q1-Q7 handling,
v1 scope guards, safety invariants, and next PoC priorities.
Co-authored-by: okuma <o0kuma@users.noreply.github.com>
Resolves the open Q1-Q7 questions as tentative decisions (self-app beta
first, target consumers, MVP scenario = read-receipt relief + group-chat
catch-up, autonomy capped at L0-L2) and builds the standard deliverable
set on top of them, ready for review at the next meeting.
Captures the idea-meeting doc and a step-by-step planning process:
confirm the open decisions (Q1-Q7), narrow to one MVP scenario,
sequence technical validation, and phase autonomy levels (L0-L4)
before writing a full PRD.
Claude
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