ESP32-S3 Bench Guide
ESP32‑S3 N16R8 벤치 가이드: 실시간 핀아웃, 강의, 시뮬레이터 및 벤치 로그
개인 계정의 공개 저장소 소유자는 GitHub로 로그인해 운영자를 확인하고 소개를 직접 관리할 수 있습니다. 관리자 승인은 필요하지 않습니다.
로그인한 뒤 확인 파일을 만들어 이 저장소의 기본 브랜치에 30분 안에 커밋해 주세요. 다른 브랜치나 포크의 파일은 인정하지 않습니다. 파일을 올린 뒤 아래에서 확인하면 관리자 승인 없이 관리할 수 있습니다. 자격을 유지하려면 파일을 남겨 두세요.
로그인이 필요하면 로그인 화면으로 이동합니다. 로그인 후 이곳으로 돌아와 원하는 확인 버튼을 다시 눌러 주세요.
프로젝트 폴더에서 /nomorevibe verify를 실행하면 소유권을 확인하고 소개를 직접 관리할 수 있습니다.
1분 만에 확인하기메이커가 쓴 소개는 위의 한 줄이 전부입니다. 더 자세한 내용은 저장소 README에서 읽을 수 있습니다. 저장소 README ↗
README 전체를 불러오는 중입니다.
README 전문 보기 ↗This project collects peer-reviewed open-access studies whose participants were women only. It labels each study, splits it into sections, embeds the text and stores it in Supabase (pgvector) so it can be searched as a RAG.
scraper/europepmc.py) for MeSH Humans + Female, NOT Male, open access with full text. Reviews, editorials and comments are excluded.scraper/fulltext.py.scraper/label.py):
female_only_verified: MeSH is clean and no male participant terms appear in the title, abstract or methods.female_only_probable: MeSH is clean but male terms appear in the text. Check these by hand or with the LLM.female_only_unverified: no methods section was found.rejected: tagged Male or missing the Humans/Female tags.ANTHROPIC_API_KEY secret is set, Claude reads the abstract and methods and confirms or rejects each paper.papers and chunks tables, plus a match_chunks() search function (db/schema.sql). The schema is applied automatically.The search page is a static page plus two optional Vercel functions. Live at https://larsfrontiers2.vercel.app
search_evidence() in Supabase directly with the public read-only key.search_evidence() is a hybrid search. It combines full-text matching (stemmed, phrases, OR) with semantic neighbours of the best keyword hits, using reciprocal-rank fusion, a cap of 2 passages per paper, and a boost for studies that match the user's life stage.ANTHROPIC_API_KEY set in the Vercel project, /api/expand uses Claude for that translation and /api/answer writes a cited plain-language summary.web → Deploy.Optional home server (Pi 5 + NVMe SSD). It stores the full text of every paper with a word index: every mention, exact totals, paging and CSV export. It also keeps scraping without Supabase's size limit. The website uses it through /api/fulltext when PI_API_URL + PI_API_TOKEN are set in Vercel, and falls back to Supabase otherwise. Step-by-step guide: pi/README.md.
/api/search, /api/paper (full text on demand, licence-aware) and /api/stats on the Vercel site. Tool definitions are in AGENT_TOOLS.md.
Per paper the database stores: metadata, label + evidence, a structured evidence record (Claude), the abstract, and at most 4 passages. The 4 passages are abstract + results/conclusion, and only for CC-BY/CC0 papers; other papers keep the abstract only. That's about 12 KB per paper versus ~150 KB before, so the free plan holds roughly 25–30k papers. Full text is fetched live from Europe PMC by /api/paper. Set PASSAGES_PER_OPEN_PAPER to change the 4.
scraper//db/. A setup job applies the schema, then 14 topic jobs (6 at a time) each resume where the last run stopped. Filling pauses at DB_SIZE_LIMIT_MB (400 MB, Supabase free plan ≈ 2,500 papers). Raise it after upgrading the plan.reports/latest_run.json, reports/latest_sample.md (a quality-check sample) and reports/latest_papers.jsonl.SUPABASE_DB_URL: Supabase dashboard → Connect → Session pooler connection string, with your DB password filled in. Without this secret, runs are dry runs that produce reports only.
Optional: ANTHROPIC_API_KEY turns on the LLM verifier.
pip install -r requirements.txt
export SUPABASE_URL=https://jvlrbsusjvuxsgijgxpb.supabase.co
export SUPABASE_PUBLISHABLE_KEY=sb_publishable_...
python -m rag.ask "Does resistance training improve bone density in postmenopausal women?"
commercial_use_ok is true only for CC-BY / CC0 papers. Filter on it before serving full text in a commercial product.
| Labeller | Papers reviewed | "Verified" that are really women-only participant data | Notes |
|---|---|---|---|
| v1 (MeSH + male-word scan) | 60 | ~53% (18–19 of 35) | Essays, cell-line work, database builds and mixed-sex trials slipped through |
| v2 (+ methods must mention women, mixed-% detection, animal rejection) | 150 | ~91% (~115 of 127) | ~85% of papers were pregnancy/maternal (ranking bias) |
| v3 (+ 14-topic rotation, staff-interview flag, non-primary rejection) | 140 | ~95% (~98 of 103) | Balanced across topics; remaining misses: guidance papers, lab studies with small patient samples, child-outcome DHS analyses |
Known limits: n_female is regex-extracted and sometimes wrong. About half of the "probable" papers are actually fine (held back conservatively). Adding the ANTHROPIC_API_KEY secret turns on the LLM verifier, which fixes both.
/chat (page) + /api/chat (Vercel, region fra1). A research guide that explains what women-only studies found, matched to the person's situation, under strict guardrails (web/api/_guardrails.js):
ANTHROPIC_API_KEY in Vercel (models ANSWER_MODEL, default claude-sonnet-5-5, and EXPAND_MODEL, default claude-haiku-5-5). To keep the AI step inside the EU, switch the Claude call to Amazon Bedrock's EU profile.