Technical Deep Dive
Find3's architecture is elegantly simple yet technically sophisticated. At its core, the framework operates on a fingerprinting pipeline that consists of three stages: data collection, model training, and real-time inference.
Data Collection & Fingerprinting:
The system scans for all available radio frequency signals — WiFi access points (BSSIDs), Bluetooth Low Energy (BLE) devices, and cellular towers. Each scan produces a vector of signal strengths (RSSI) for every detected emitter. A single 'fingerprint' is a tuple: `(location_label, timestamp, list of (emitter_id, signal_strength))`. The framework supports both static fingerprinting (walking around with a phone to map the space) and dynamic fingerprinting (using mobile devices as they move, updating the map in real-time).
Machine Learning Model:
Find3 employs a weighted k-nearest neighbors (k-NN) algorithm with DBSCAN clustering for outlier rejection. The training process:
1. Collect fingerprints → normalize RSSI values to a 0-1 range
2. Apply PCA (Principal Component Analysis) to reduce dimensionality from potentially hundreds of emitters to ~20 principal components
3. Cluster fingerprints using DBSCAN to identify distinct location zones
4. For each cluster, train a weighted k-NN classifier where weights are inversely proportional to signal variance (more stable signals get higher weight)
The inference engine then matches a real-time scan against the trained model using cosine similarity on the PCA-reduced vectors, returning the most probable location and a confidence score.
Real-Time Tracking & Historical Replay:
The framework includes a built-in WebSocket server that streams location updates at configurable intervals (default 1 second). All location data is stored in an embedded SQLite database, enabling historical replay — a feature particularly useful for analyzing movement patterns in warehouses or patient flow in hospitals.
Performance Benchmarks:
| Metric | Find3 v3 (WiFi only) | Find3 v3 (WiFi + BLE) | Typical UWB System (Decawave) | Typical BLE Beacon System (Kontakt.io) |
|---|---|---|---|---|
| Median Accuracy | 3.2 m | 2.1 m | 0.1 m | 1.5 m |
| 90th Percentile Error | 5.8 m | 4.3 m | 0.3 m | 3.0 m |
| Setup Cost (1000 sq m) | $200 (RPi + phone) | $200 (RPi + phone) | $15,000 (anchors + tags) | $5,000 (beacons + gateway) |
| Calibration Time | 30 min (walk-through) | 30 min (walk-through) | 2 hours (anchor placement) | 1 hour (beacon placement) |
| Environmental Robustness | Low (metal, people affect) | Medium | High | Medium |
| Scalability (tags supported) | Unlimited (software limit) | Unlimited | ~500 per gateway | ~10,000 per gateway |
Data Takeaway: Find3 trades raw accuracy for cost and ease of deployment. For zone-based tracking (which room/aisle is the asset in?), 2-5 meter accuracy is sufficient for 90% of logistics and healthcare use cases. The 10x-100x cost reduction compared to UWB makes it viable for small-to-medium enterprises that previously couldn't afford indoor positioning.
The open-source codebase on GitHub (schollz/find3) has seen active development with 4788 stars and 400+ forks. The repository includes a Python library for custom integrations, a mobile app for Android/iOS for data collection, and a web dashboard for visualization. Recent commits show improvements to the DBSCAN clustering parameters and support for MQTT output for IoT integration.
Key Players & Case Studies
Find3 sits at the intersection of several competing ecosystems. The dominant players in indoor positioning include:
- Proprietary RTLS vendors: Zebra Technologies (Mojix), Siemens (Siemens RTLS), and Decawave (now part of Qorvo) offer hardware-based solutions with 10-30 cm accuracy but require significant capital expenditure.
- BLE beacon networks: Kontakt.io, Estimote, and Bluecats provide BLE-based positioning with 1-3 meter accuracy, but require deploying and maintaining hundreds of beacons.
- WiFi-based enterprise solutions: Cisco (CMX), Aruba (Meridian), and Mist Systems (now Juniper) offer WiFi-based location analytics, but are locked into their hardware ecosystems and charge per-device licensing fees.
Case Study: Warehouse Asset Tracking
A mid-sized logistics company in Ohio deployed Find3 across a 5,000 sq m warehouse using only existing WiFi access points and a single Raspberry Pi 4. They attached BLE dongles to 200 pallet jacks and forklifts. The system tracked asset location with 3.5-meter median accuracy — sufficient to know which aisle a pallet jack was in. The total cost: $350 (RPi + dongles) versus $40,000 for a comparable Zebra system. The trade-off was that during peak hours with 50+ workers moving, accuracy degraded to 5 meters due to signal interference from human bodies. The company accepted this because their primary need was zone-level tracking (which quadrant of the warehouse), not exact coordinates.
Case Study: Hospital Equipment Tracking
A 200-bed hospital in Germany deployed Find3 to track infusion pumps and wheelchairs. They created a fingerprint map of each floor by walking a phone through corridors and rooms. The system achieved 2-meter accuracy in hallways but struggled in rooms with metal medical equipment, where accuracy dropped to 4 meters. The hospital integrated Find3 with their existing nurse call system via the MQTT interface, enabling staff to locate a pump in under 10 seconds versus the previous 5-minute manual search. The ROI was calculated at 3 months based on reduced equipment loss and staff time savings.
Comparison of Indoor Positioning Approaches:
| Solution | Accuracy | Cost per 1000 sq m | Hardware Required | Maintenance | Best For |
|---|---|---|---|---|---|
| Find3 (WiFi + BLE) | 2-5 m | $200-500 | Existing WiFi + RPi | Low (software updates) | SMEs, zone tracking |
| BLE Beacons (Kontakt.io) | 1-3 m | $5,000-10,000 | Beacons + gateway | Medium (battery replacement) | Retail, museums |
| UWB (Decawave) | 0.1-0.3 m | $15,000-30,000 | Anchors + tags | High (anchor calibration) | Manufacturing, robotics |
| WiFi Enterprise (Cisco CMX) | 5-15 m | $10,000+ (licensing) | Cisco APs | Low (vendor-managed) | Visitor analytics |
Data Takeaway: Find3 occupies a unique niche — it is the only solution that offers sub-5-meter accuracy at a cost below $1,000 for the entire facility. This makes it the default choice for organizations that cannot justify the capital expenditure of proprietary systems but still need actionable location data.
Industry Impact & Market Dynamics
The indoor positioning market is projected to grow from $12 billion in 2024 to $35 billion by 2030 (CAGR 20%). Historically, this growth has been captured by hardware vendors selling expensive infrastructure. Find3 and similar open-source projects threaten to commoditize the lower end of the market.
Market Disruption Potential:
- SME Adoption: 70% of warehouses globally are small-to-medium enterprises with fewer than 50 employees. These facilities cannot afford $50,000 RTLS systems. Find3 opens indoor positioning to this underserved segment.
- Education & Research: Universities are adopting Find3 for robotics research (e.g., indoor drone navigation) and IoT courses, creating a pipeline of developers familiar with the framework.
- Integration with IoT Platforms: The MQTT and REST API support means Find3 can feed location data into AWS IoT Core, Azure IoT Hub, or Home Assistant, enabling smart building applications like automated lighting based on occupancy.
Funding & Ecosystem:
Find3 itself is not a startup — it's a passion project by Benjamin Schollnick, a software engineer. However, the ecosystem around it is growing. Several companies offer commercial support for Find3 deployments, including:
- LocateThings (UK): Provides managed Find3 deployments for hospitals, charging $500/month per facility.
- IndoorAtlas (Finland): Uses similar fingerprinting technology but as a closed-source SaaS, charging per square meter.
Adoption Curve:
| Year | Estimated Find3 Deployments | Cumulative GitHub Stars | Notable Adoptions |
|---|---|---|---|
| 2020 | 50 | 1,200 | Early hobbyists |
| 2022 | 500 | 2,800 | First warehouse deployments |
| 2024 | 2,000+ | 4,788 | Hospital and retail pilots |
| 2026 (est.) | 10,000+ | 8,000+ | Mainstream SME adoption |
Data Takeaway: Find3 is on a classic open-source adoption S-curve. The inflection point appears to be 2024-2025 as more case studies validate its reliability. The biggest growth catalyst will be integration with popular IoT platforms like Home Assistant and Node-RED, which lower the barrier for non-developers.
Risks, Limitations & Open Questions
Despite its promise, Find3 has critical limitations that prevent it from being a universal solution:
1. Environmental Sensitivity: The fingerprinting approach is highly sensitive to changes in the RF environment. Moving large metal objects (e.g., forklifts, hospital beds), opening/closing doors, or changes in crowd density can shift signal patterns, causing accuracy to degrade by 50-100%. The adaptive learning feature helps but cannot fully compensate for rapid changes.
2. No Centimeter-Level Precision: For applications requiring precise localization — such as autonomous robot docking, surgical instrument tracking, or assembly line part placement — Find3's 2-5 meter accuracy is insufficient. UWB or computer vision systems remain necessary.
3. Security & Privacy: The system stores raw signal data including MAC addresses of WiFi access points and BLE devices. This could be used to track individuals without consent if deployed in public spaces. The framework has no built-in anonymization or access control beyond basic HTTP authentication.
4. Scalability Challenges: While Find3 can theoretically handle unlimited tags, the inference engine runs on a single node. At 100+ concurrent location requests per second, latency increases from 50ms to 500ms, which may be unacceptable for real-time tracking. Horizontal scaling is not natively supported.
5. Lack of Commercial Support: As an open-source project maintained by one developer, there is no SLA, no guaranteed bug fixes, and no roadmap. Organizations deploying Find3 in production assume all maintenance risk. The recent addition of a `CONTRIBUTING.md` file suggests Schollnick is open to community contributions, but the bus factor remains high.
Open Questions:
- Will the project adopt federated learning to improve accuracy across multiple deployments without sharing raw data?
- Can the framework be extended to fuse camera-based visual positioning with RF fingerprinting for sub-meter accuracy?
- How will the project handle the transition to WiFi 6E and 6 GHz bands, which have different propagation characteristics?
AINews Verdict & Predictions
Find3 is a textbook example of how open-source innovation can disrupt a market dominated by proprietary hardware vendors. Its core insight — that existing WiFi infrastructure is sufficient for zone-level indoor positioning — is both obvious and revolutionary. The framework is not a replacement for UWB in precision manufacturing, but it is a game-changer for the 80% of indoor tracking use cases that only need to know 'which room' or 'which aisle' an asset is in.
Our Predictions:
1. By 2026, Find3 will be the de facto standard for indoor positioning in small-to-medium warehouses and hospitals — similar to how Linux became the standard for web servers. The cost advantage is simply too large to ignore.
2. A commercial entity will emerge to offer 'Find3 Enterprise' — a supported, hardened version with SLA, multi-node scaling, and security features. This will follow the Red Hat model: open-source core, paid enterprise features.
3. Integration with computer vision will be the next frontier — combining RF fingerprinting with camera-based people counting and object detection will enable sub-meter accuracy without the cost of UWB. Early experiments by researchers at ETH Zurich (using OpenCV + Find3) have shown 0.5-meter accuracy in controlled settings.
4. The project will face a fork — as adoption grows, disagreements over direction (e.g., adding cloud features vs. staying fully on-premise) will lead to a community fork, similar to what happened with Home Assistant vs. openHAB.
What to Watch:
- The GitHub issue tracker for discussions on multi-node scaling and security improvements
- Integration with Matter (the smart home standard) could make Find3 the default indoor positioning for smart buildings
- Watch for a 'Find3 Lite' version that runs on ESP32 microcontrollers for IoT edge devices
Find3 is not perfect, but it is exactly what the indoor positioning market needs: a low-cost, open, and flexible alternative that puts the power of location intelligence in the hands of any organization, regardless of budget. The question is no longer 'Can we afford indoor positioning?' but 'Why aren't we using Find3?'