One small, honest ML system per day, each built end to end on Hopsworks. Same shape every time: an FTI (feature, training, inference) pipeline, a real result with its caveats, and a served model you can poke at. No notebooks-that-never-ship, no accuracy without a holdout, no demo wired to a mock.
| # | system | the question | result | published | repo |
|---|---|---|---|---|---|
| 001 | README Vaporware Score | does a repo get abandoned, from its README text alone? | ROC-AUC 0.76 | 2026-06-29 | readme-vaporware-score |
| 002 | Asteroid Doomsday-o-meter | how big is an asteroid (so, how dangerous), from its Gaia spectrum alone? | size error ×1.13 vs ×1.34 blind | 2026-06-30 | asteroid-size-from-light |
| 003 | Phishing at Issuance | is a freshly issued TLS certificate phishing, from its hostname alone? | ROC-AUC 0.78 holdout vs 0.50 blind | 2026-07-01 | phish-at-issuance |
| 004 | Where on Earth | which country was a photo taken in, from its pixels alone? | top-1 52.3% / top-5 79.8% over 173 countries vs 21.2% zero-shot | 2026-07-02 | where-on-earth |
| 005 | How Predictable. | can a machine learn your taste in 30 clicks, live, in front of you? | crowd prior 0.719 pairwise vs 0.511 zero-shot; per-user Bayesian layer climbs on-screen | 2026-07-03 | how-predictable |
| 006 | Live Sky Watch | where will every aircraft over Europe be in 60/180/300 s, and which one is not behaving like traffic here? | live same-sample: model 964 m vs physics 1427 m at 60 s where it intervenes; jamming grid + learned normalcy | 2026-07-06 | live-sky-watch |
| 007 | Ghost Fleet | which vessels behave like the sanctioned shadow fleet, from their AIS tracks alone? | 9.4x lift over a blind sanctions-list lookup, ROC-AUC 0.92 (population split); live network reveal | 2026-07-07 | ghost-fleet |
| 008 | the untested | which never-tested plant might fight a drug-resistant infection, from molecular structure alone? | mean AMR ROC-AUC 0.80, beats 1-NN Tanimoto on every scored head; recovers Artemisia for malaria from structure alone | 2026-07-08 | the-untested |
| 009 | downwind | what is in the air where nobody is measuring? | PM2.5 20.9% RMSE under the raw CAMS prior at leave-stations-out stations (r2 0.61 vs 0.38); live all-Europe field with a monitored-vs-predicted frontier | 2026-07-09 | downwind |
Every repo in the series follows the same mould, so they read as siblings.
Shape. An FTI system on Hopsworks. Sources to a feature pipeline to a
Feature Group, a Feature View to training to the Model Registry, a deployment to
an endpoint, an app that calls it. The skeleton lives in
templates/diagram.mmd.
Banner. Generated, not hand-drawn, so 30 of them stay consistent. Dark canvas, emerald accent, the Hopsworks hop-mark as the fixed brand, only title/tagline/emoji/index change per repo.
python tools/make_banner.py \
--title "My System" \
--tagline "What it predicts, in one honest sentence." \
--emoji "🧪" --index 002 --out assets/banner.svgREADME. Result first (with the metric and the holdout), then caveats, then
architecture (the diagram plus a file-by-file map), then reproduce, then the
served demo. Start from templates/README.template.md.
Honesty rules. The label is named and its proxy is stated. There is a holdout number, not just cross-validation. No feature leaks the label. Heavy fits run as Hopsworks jobs, not in a terminal. Feature extraction is one shared function so training and serving cannot skew.
mkdir ../my-new-system && cd ../my-new-system
cp -r ../awesome-ml-systems/tools . # the banner generator
cp ../awesome-ml-systems/templates/README.template.md README.md
python tools/make_banner.py --title "..." --tagline "..." --index NNN
# fill the README, paste templates/diagram.mmd, then add a row to the table above