BeamNG.Earth
A 1:1 scale streamed Earth for BeamNG.drive: real elevation and satellite imagery, floating origin, LOD streaming from a companion server
Real Wild Rift win rates from the top 50 players on every champion, plus a Build Studio that generates optimal items, runes and summoners for your exact game.
GamesPython · TypeScriptGitHub @AmirDiceOnlineOwner unconfirmed
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Production LLM build advisor at wrtruemeta.com. Next.js + Python, a Gemini-backed recommendation service running as its own deploy, Redis quota and usage analytics, and an automated first-party data pipeline.
Taken from the repository README
Loading the full README. The saved excerpt is shown below.
The repository behind wrtruemeta.com: a Wild Rift
meta tracker built on first-party data -- the win rates of the top players on
every champion's leaderboard, captured from the game itself, not aggregated
from other sites.
The admin page on the site has an Operations panel: scrape, extract, refresh
exports, fetch patch notes, publish, and redeploy the advisor are all one
click. Those buttons are executed on the collection machine by:
which also starts hidden at logon via a Startup-folder launcher. Jobs queue
while it is offline.Read the full README ↗The repository behind wrtruemeta.com: a Wild Rift meta tracker built on first-party data -- the win rates of the top players on every champion's leaderboard, captured from the game itself, not aggregated from other sites.
phone (ADB) -> src/ scraper -> data/captures/ -> extraction -> data/*.json|csv
|
scripts/ exports -> web-next/src/data/ -> the site
|
web/ + api/ -> the build advisor (own deploy)
| Piece | What it is |
|---|---|
src/ | The ADB scraper: captures leaderboard/profile/build frames from a real device, plus the offline frame extractor (extract_frames). |
data/ | Everything captured and derived: win rates, items, runes, champions, ability formulas, the official patch archive. |
scripts/ | The pipeline: exports (export_captures, export_json, export_engine_data, export_champion_details, build_ladder_pulse), LLM extraction (extract_formulas), patch appliers (apply_patch_*, which assert before writing), the advisor deploy, and the admin ops runner. |
web-next/ | The site: Next.js, deployed on Vercel from origin/main. Reads only committed JSON under web-next/src/data/. |
web/ + api/ | The build advisor: a Python serverless function (own Vercel project, deployed by scripts/deploy_advisor.py --deploy) that generates builds with an LLM grounded in the scraped data. |
tests/ | Data-integrity tests, run by CI on every push. No API keys in CI, so nothing there can scrape or call an LLM. |
The admin page on the site has an Operations panel: scrape, extract, refresh exports, fetch patch notes, publish, and redeploy the advisor are all one click. Those buttons are executed on the collection machine by:
python -m scripts.ops_runner
which also starts hidden at logon via a Startup-folder launcher. Jobs queue while it is offline.
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements-scrape.txt # scraper + pipeline
pip install -r requirements-dev.txt # test suite
requirements.txt alone is the advisor function's slim bundle -- do not grow
it casually; the serverless bundle has a 250MB ceiling.
OCR needs the Tesseract binary (winget install UB-Mannheim.TesseractOCR);
Gemini-engine extraction reads GEMINI_API_KEY from web-next/.env.local.
Scraping needs ADB and a connected device with Wild Rift installed. Secrets
live in web-next/.env.local (gitignored) and the Vercel projects, never in
the repo.
Applying a balance patch touches five surfaces, and history shows missing one
goes unnoticed for weeks: advisor sources, data/runes.json -> engine export,
champion details export, change history + summary, and formula re-extraction
for changed champions -- then a cache-version bump and an advisor redeploy.
The scripts/apply_patch_*.py scripts document every applied delta and assert
current values before writing, so a double-run or a shifted base fails loudly.