ruview vs timesfm
Two of the top projects in Open models, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
ruview
WiFi CSI sensing on ESP32 for presence, breathing, heart rate and pose
timesfm
Pretrained time-series foundation model for zero-shot forecasting from Google Research
Verdict
ruview ranks higher, 95 to 77. Of the 4 score parts we measure for both, ruview leads on 3, timesfm on none and 1 is even.
- Adoptionruview leads by 12 points, 98 to 86.
- FreshnessEven at 100.
- Maintenanceruview leads by 28 points, 88 to 60: a wide gap.
- Agent-readyruview leads by 55 points, 85 to 30: a wide gap.
Pick ruview if you are
Self-hosters and researchers needing camera-free presence and vitals sensing
- Runs on roughly $9 ESP32 nodes; the pretrained encoder fits in 8 KB at 4-bit
- Publishes 21 Home Assistant MQTT entities per node, plus Matter and Apple Home bridges
Pick timesfm if you are
Engineers and researchers forecasting time series without per-task training
- Zero-shot forecasting of univariate and multivariate series with past-only and future covariates
- Returns point forecast plus 9 quantiles (0.1 to 0.9) per step
| What we compare | ruview | timesfm |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 98, widely used | 86, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 88, healthy | 60, fair |
| Agent-ready | 85, ready | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 101.9k | 35k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Sep 2026 |
| Last release | Oct 2026 | Aug 2026 |
| Language | Rust | Python |
| Docker | Yes | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Mentioned |
ruview
RuView reads Channel State Information from ESP32-S3/C6 nodes and derives presence, person count, breathing (6–30 BPM), heart rate (40–120 BPM), falls and 17-keypoint pose without cameras. It is written in Rust, with pretrained models on Hugging Face, Home Assistant, Matter and HomeKit integration, and an npm MCP toolkit for AI-guided setup. Several accuracy figures are self-reported, and the README says the on-device pose model is a first cut.
Who it is for: Self-hosters and researchers needing camera-free presence and vitals sensing
Strengths
- Runs on roughly $9 ESP32 nodes; the pretrained encoder fits in 8 KB at 4-bit
- Publishes 21 Home Assistant MQTT entities per node, plus Matter and Apple Home bridges
- Pretrained weights on Hugging Face, plus a phase-variance presence fallback needing no model
- README retracts an earlier accuracy claim and flags unvalidated vitals and synthetic RF accuracy
Weaknesses
- Live sensing needs ESP32-S3/C6 CSI firmware; the Docker image runs simulated data
- Committed on-device pose model scores PCK@20 of 3.0% and its runtime path is a stub
- Through-wall range is about 5 m and signal-dependent
- Very large README and feature surface; hard to tell which parts are production-ready
- GPU optional
- Docker + Compose
- Needs ESP32-S3 or ESP32-C6 boards, Cognitum Seed (optional), Home Assistant / MQTT (optional)
- Models: safetensors, RVF, Candle
- port 3000
timesfm
TimesFM is a pretrained decoder-only model for time-series forecasting, installed as a Python package with PyTorch or MLX (Apple silicon) backends. Version 3.0 forecasts univariate and multivariate series with past-only and past-and-future covariates, returning point forecasts plus nine quantiles (0.1 to 0.9). The code is Apache-2.0, but 3.0 weights carry a non-commercial license.
Who it is for: Engineers and researchers forecasting time series without per-task training
Strengths
- Zero-shot forecasting of univariate and multivariate series with past-only and future covariates
- Returns point forecast plus 9 quantiles (0.1 to 0.9) per step
- MLX backend runs 3.0 on Apple silicon without PyTorch, numerically matched to PyTorch
- Includes LoRA fine-tuning example (HF Transformers + PEFT), unit tests and an agent SKILL.md
Weaknesses
- TimesFM 3.0 weights are non-commercial and non-production; commercial use only via Google Cloud services
- Not an officially supported Google product
- Forecasting only; contexts beyond 15,360 points are truncated to the most recent
- README code imports differ between 3.0 examples and the PyPI 2.0.2 note, which is unclear
- GPU optional
- Needs PyTorch or MLX, Hugging Face checkpoints
- Models: TimesFM 3.0 (PyTorch checkpoint), TimesFM 2.5 (200M params, 16k context), TimesFM 1.0 and 2.0 (v1 archive)
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