Survival LLM
A free AI assistant and survival library (1.5 million offline articles) that runs on your own computer, with no internet. Shows the source of every answer. Mac and Windows.
슬라이더를 움직여 이적료를 확인하고, 만든 빌드를 공유해 보십시오.
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Football transfer fee predictor with SHAP-based stat-improvement counterfactuals (side project)
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Taken from the repository README
Loading the full README.
Read the full README ↗A sports valuation & prediction platform. Two data/modeling domains — soccer (transfer-fee model, squad-strength sim engine, and the apps built on it) and KBO baseball (sabermetric season/postseason sim) — feed a Next.js web app backed by Supabase.
The project began as a single soccer transfer-fee notebook and grew into several Python modules plus a production web app. It was reorganized into the layout below; see ARCHITECTURE.md for the full system + data-flow map.
azz3/
├── python/ # all Python (installed as one editable package: `valuetrack`)
│ ├── pyproject.toml
│ ├── core/ # soccer transfer-fee model → import core.*
│ │ ├── config.py # REPO_ROOT / DATA_DIR / WEB_* paths
│ │ └── cli/ # train · predict · explain · export_for_web · …
│ ├── soccer/
│ │ ├── engine/ # shared squad-strength / synergy / match sim engine
│ │ └── apps/ # worldcup (winner) · premier_league · worldcup_stars · destination
│ ├── kbo/ # self-contained KBO pipeline (src/ + scripts/)
│ ├── data/ # local model cache + trained model (gitignored)
│ ├── predictions/ # committed demo run artifacts
│ └── tests/ # pytest (covers core.* + kbo.src.*)
├── web/ # Next.js 14 app (Vercel) — reads Supabase + exported JSON
├── supabase/ # Postgres migrations (auth, payments, kbo_*, soccer_*, api_tokens)
└── docs/ # design notes, model report, Open News API spec, assets/
The web app lives at the repo root (Vercel roots there and bundles an embedded Python
serverless function under web/api/). Python packages resolve web/ via
core.config.REPO_ROOT (walks up to .git), so python/ can move without breaking exports.
# Python (CPython 3.9)
python3 -m venv .venv
.venv/bin/pip install -e "python/[dev]" # installs core, soccer.*, kbo.* + dev tools
brew install libomp # macOS only — xgboost needs it
# Web
cd web && yarn install # Yarn 4 (Berry), Node >= 20
cp .env.example .env.local # fill in Supabase + KakaoPay keys
There is no more root requirements.txt; Python deps live in python/pyproject.toml.
web/requirements.txt is separate (it pins the Vercel Python serverless runtime).
# --- soccer core model (writes into web/public + web/api/model) ---
.venv/bin/python -m core.cli.train
.venv/bin/python -m core.cli.export_for_web
.venv/bin/python -m core.cli.export_real_players
# --- soccer sim apps ---
.venv/bin/python -m soccer.apps.worldcup.run_prediction --sims 1000000
.venv/bin/python -m soccer.apps.premier_league.run_prediction --sims 1000000
.venv/bin/python -m soccer.apps.worldcup_stars.run_stars
.venv/bin/python -m soccer.apps.destination.run_recommender
# --- KBO ---
.venv/bin/python -m kbo.scripts.run_prediction
# --- web (Next.js + local Python inference bridge) ---
cd web && yarn dev # web only
cd web && yarn dev:full # web + local Python model server
.venv/bin/python -m pytest python/tests -v # Python
cd web && yarn test # web (Vitest)
cd web && yarn typecheck && yarn build # web type + build check
CLAUDE.md — repo conventions for AI-assisted work (gstack).