ESP32-S3 Bench Guide
ESP32‑S3 N16R8 벤치 가이드: 실시간 핀아웃, 강의, 시뮬레이터 및 벤치 로그
아직 감지된 업데이트가 없습니다. GitHub 릴리스가 나오면 여기에 보입니다.
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 확인하기Find USF Tampa classes by grade history, instructor, and the latest observed seats.
저장소 README에서 가져옴
README 전체를 불러오는 중입니다. 아래는 저장된 발췌입니다.
Find your next USF class.
Search courses and instructors, compare historical grade distributions, and check the latest observed seats for USF Tampa.
• Search by course code, CRN, subject, number, title, or instructor.
• Compare an instructor's past grades in that course, with sample sizes and covered terms.
• Browse GenEd requirements and see seat counts with their last-checked time.
• Open Rate My Professors links and copy a CRN for registration in OASIS.README 전문 보기 ↗Find your next USF class.
Search courses and instructors, compare historical grade distributions, and check the latest observed seats for USF Tampa.
A React/TypeScript frontend talks to a FastAPI backend backed by PostgreSQL on Supabase. A background worker refreshes USF schedule data; historical grade imports retain their source and academic term. The API, worker, and frontend are hosted on Render.
Grade history comes from USF InfoCenter reports. Missing history stays unavailable, and small samples are labeled. Past grades are not a prediction of your grade; confirm seats in OASIS. RMP links are references only—ratings and reviews are not imported or used in scoring.
Requires Node.js and npm. From the repository root:
cd web
npm ci
Copy web/.env.example to web/.env.local, keeping VITE_USE_MOCK_DATA=true, then run npm run dev. This mode uses labeled synthetic fixtures and needs no database.
Requires Python 3.12+, uv, Node.js, and Docker.
uv sync from the repository root..env.example to .env. The included connection string targets local Docker PostgreSQL.docker compose up -d db
uv run alembic upgrade head
uv run uvicorn easy_a.api.app:app --reload
web/.env.local:VITE_USE_MOCK_DATA=false
VITE_API_BASE_URL=http://localhost:8000
npm ci and npm run dev from web/.A fresh database is empty. Schedule and approved aggregate grade data must be imported separately; production source exports are not included. See the operations runbook and ingestion scripts.
From the repository root:
uv run pytest
uv run ruff check .
uv run mypy src migrations scripts tests
From web/:
npm test
npm run lint
npm run typecheck
npm run build
PostgreSQL integration tests require a separate test database via EASY_A_TEST_POSTGRES_URL.
GET /api/v1/search finds instructors by course; /api/v1/rankings/search accepts a course_id
and exact listed instructor to show their sections. See /docs on the API for parameters.
Displayed A share is A / (A + B + C + D + F), accompanied by the observed grade count. It is distinct from the backend's historical easiness score.
The backend combines normalized grade favorability (80%) and the complement of withdrawal rate (20%), using Bayesian shrinkage for small samples. Seats, GenEd requirements, modality, syllabus signals, and RMP links do not affect that score.
Instructor scoring requires at least 30 effective graded students and a usable instructor assignment in the same course. Grade favorability is shrunk toward the course mean with a prior strength of 30; course-level grade priors and withdrawal priors remain 60.
Laboratory sections use course-level history and are excluded from instructor-level statistics. Staff and blank names are never assigned to a named instructor. Names are matched within a course, not pooled across courses. Single-term histories are labeled without an extra scoring penalty.
When instructor evidence is insufficient, scoring falls back through course, subject, and global history. A subject or global fallback is never presented as observed course history. Confidence labels describe data coverage, not certainty.
Implementation: analytics · rankings
Only approved aggregate grade data and its provenance belong in the application. Never commit raw grade exports, student-level records, credentials, or .env files. Tests use synthetic fixtures.
Independent student project. Not affiliated with or endorsed by the University of South Florida.