kootenpv/whereami
Uses WiFi signals :signal_strength: and machine learning to predict where you are observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3636
- days_rel: n/a
- days_push: 1007
- n_releases_24m: 0
Adoption not part of the score
5139 stars · 251 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
A Python CLI tool that uses WiFi access point signal strengths and a scikit-learn RandomForest model to predict your indoor location. It learns labeled location samples (e.g., bedroom, kitchen) and can distinguish positions as close as 2-10 meters apart.
Use cases
- predict which room of the house I'm in from wifi signals
- train a model to recognize my desk vs couch location
- get indoor location estimates for home automation
- check cross-validated accuracy of wifi-based location learning
- pipe predicted location into other scripts or voice output
- distinguish locations a few meters apart using access point signals
When to choose
- you want a simple, cross-platform indoor positioning tool using existing wifi hardware
- you need location prediction usable from the command line or as a Python library
- you want to label a handful of fixed indoor spots and classify between them
When to avoid
- you need GPS-quality or continuously tracked outdoor positioning
- you require real-time streaming location updates rather than on-demand predictions
- you need a mobile app or production-grade indoor positioning system
Facets
cli-tool · maturity maintenance
machine-learning cli data-science machine-learning developer-tools windows python cli cross-platform wifi-signals indoor-positioning random-forest sklearn location-prediction command-line macos linux
2 sources
- readme: https://github.com/kootenpv/whereami · fetched 2026-08-28 · dbd769a5a9ed
- registry_pypi: https://pypi.org/pypi/whereami/json · fetched 2026-08-29 · 18884db70755
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| kootenpv/whereami | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="kootenpv/whereami")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem