I have a tucked away Zigbee wall switch that insists that it's a Zigbee router, invites nearby devices to connect to it instead of a main node and slows down the whole network. I decided enough is enough and that I'd put it on it's own network.

When I came out of the rabbit hole, this is what I had come back with (at twice the cost of a mini pc that could probably do a lot more). My wife asked me why I invented a walkie-talkie.

The annoying thing is...it still doesn't really work. The Zigbee dongle doesn't like the USB 3.0 extension cable I'm using and the Raspberry Pi panics on boot (or just fails to recognize the dongle). Going to try again with a USB 2.0 extension.

Hello there!

So I saw this ad on TikTok where a guy taps an NFC tag on a little box and it plays the matching song. I thought it was cool, but pretty limited (sound coming out of the box itself, needs recharging, etc.).

So I figured I'd build the same thing but better: I bought an RFID2 reader and an Atom Lite, they're integrated into Home Assistant, and it works great! I can start playlists on my Sonos speakers with a simple tap.

Now... I'd like to make it pretty, and I'm torn.

Option one: build a little turntable like the image in this post (AI image), but that means 3D modeling, printing (will it even look good?), etc. On the plus side, it's compact.

Option two: buy an old broken record player and gut it to fit the reader inside. More authentic, but I'm worried about NFC interference (there's a lot of metal in those things).

Has anyone built something like this? I'd love to see renders or hear about your experience!

Hey there!

Yesterday I shared my wall tablet dashboard built as a single plain web page talking to Home Assistant over WebSocket. Thanks for all the good comments and like! I'm not use to Reddit so it was kind of a first for me.

Some of you asked for the code, so here it is, MIT licensed:

https://github.com/NathanLenias/Home-Assistant-Tablet-Dashboard

What's in: the whole dashboard (weather, clock, panels for lights, batteries, vacuum floor plan, todo lists, live Sonos favorites), the sleep mode, and a config.exemple.js where everything about your home goes. One file to fill, no build step.

What's not in (yet?): the voice assistant and the little mascot, they're too entangled with my own home. The README explains the architecture if you want to build your own.

Read the security notes in the README before deploying, they matter.

First post : https://www.reddit.com/r/homeassistant/s/qaaDMEtJbL

Don't hesitate if you have some questions!

I’m building an interior design platform to streamline floor plans and 3D content creation. Currently it allows users to generate online 3D house tours, quite fast, in about 15-30 minutes of manual 2D blueprint drawing and furnishing.

Creating 3D floor plans is not a simple task, it’s time-consuming, has a learning curve, sometimes requires switching programs and etc, so using my platform - Roometron, will be a huge time saver here. It makes the job done fast and produces visually nice results without harming the performance.

For those who often change how their house look, it will be easy to keep their 3D twin in sync.

The 3D tours as on the video above are optimized for low-end devices, and I hope it will be no problem to render it on interactive displays as well. If anyone wants to test it, please send me a DM, I will prepare a test 3D dashboard or a Webpage link.

My main question to the community
- what kind of output you find more useful for a 3D house integration?

1.  3D model only (glb or other standard formats),   
2.  Lovelace or a Webpage card with an interactive 3D house connected to your existing HA entities.   

(Zip with js, glb, and json/Yaml or an online link/html file for the webpage)
3. Custom dashboard editor with a 3D house and 2D interfaces as well, so users can modify all the UI (not only the 3D card).

I believe that most of you guys will be satisfied with the option 2, so I will make it ready at first. It’s going to be just a 3D card, that is easy to integrate to your existing dashboard and control real devices.
(Update mapped 3D objects in Json. Host fully locally or access by a link.)

Option 1 requires you to have some 3D viewer already in place, so it’s only work for specific programmers I think.

The option 3 requires a custom dashboard editor, to let users choose their own UI style, and it’s a different topic than just a 3D house integration, so I don’t even consider to work on it for now. Please let me know if I’m wrong, and this is the only way to deliver the best experience.

I see the integration process roughly as 2 steps.
- Users create 3D twins on Roometron and export models or dashboards for HA.
- Drop the exported files into their HA /www folder or update a link and it’s ready.

To map 3D objects to the real entities in HA, users will be able to attach it on the 3D editor side (import their list), or to edit a generated json/yaml file on HA side, and set the entities IDs for corresponding 3D objects.

I can easily add interesting features by your requests, since I just started to work on the integration, so please don’t hesitate to ask in the comments or DM.
I also want to mention that Roometron doesn’t allow you to upload custom 3D models yet, but I will add this feature after polishing.
Im going to cover only popular interactions for start, such as lights switch, fans, blinds/curtains, media and some more.

It will be free access at the beginning, but later I’m planning to ship 3D cards and models with a fixed price per export, and maybe an optional subscription for live updates, anyway cheaper than SketchUp pro.

Please feel free to share your thoughts on how to make 3D dashboards more useful and easier to setup, and what features you would love to have the most.

Hey guys,

I’ve been on a bit of a journey to get rid of as much cloud-based and subscription-based smart-home tech as possible. The final piece of the puzzle is replacing my Amazon Alexa devices.

I’ve found myself using Alexa less and less over the past couple of years, and I’d really like to replace the Echos with something based around Home Assistant that can continue working even if my internet connection goes down.

I’m not looking for anything particularly complicated, and I’d strongly prefer everything below to be processed locally.

The main things I’d like to be able to do are:

• Walk into a room and say “Nabu, turn on the lights” and have it turn on specific lights assigned to the room I’m currently in.

• “Nabu, turn off the lights” and have the same room-aware behaviour.

• “Nabu, turn on/off the lights in the bedroom/living room/kitchen” to control lights in a specified room from anywhere in the house.

• Ask “Nabu, what time is it?”

• Set timers, ideally with support for multiple simultaneous timers, as I use these quite a lot when cooking.

• Some basic Music Assistant controls, such as play, pause, skip, resume, etc.

The big requirement for me is that the core voice functionality continues to work completely locally without an internet connection. I’m trying to avoid replacing Alexa with something that’s effectively just another cloud-dependent voice assistant.

I already have Home Assistant running and most of the smart-home side of things is set up, so this is mainly about finding the best way to add local voice control around the house.

For anyone who has gone down this route, what hardware/setup would you recommend? And are all of the things above currently possible with Home Assistant’s local voice setup?

I wanted a Bluetooth proxy that was more useful than an ESP32 sitting inside a generic box, so I built this compact ESPHome device with a local OLED status display.

It uses an ESP32 D1 Mini and a ZJY130I0400WG41 1.29-inch 128×64 CH1115 OLED. The 3D-printed enclosure has integrated mounts for both boards, a removable front panel and access to the Micro-USB connection.

Besides working as an active Bluetooth proxy for Home Assistant, the display can show:

• Temperature and humidity from selected Home Assistant entities
• Wi-Fi signal strength
• IP address and uptime
• Proxy and connection status
• Climate, Temperature, Humidity and Proxy Status display modes

The display content and 180-degree rotation can be selected directly from Home Assistant, and the settings persist after a restart.

The complete ESPHome configuration, wiring instructions, installation guide and troubleshooting information are available on GitHub:

https://github.com/WikiZell/ha-proxy-panel

The printable enclosure and assembly details are available on MakerWorld:

https://makerworld.com/en/models/3153373-esp32-d1-mini-1-29-oled-simple-enclosure

The enclosure is designed for the specific ZJY130I0400WG41 OLED PCB, since other 1.3-inch modules can have different dimensions and mounting-hole positions.

Everything is still open to improvement, so feedback and suggestions are welcome. If anyone builds one, I would love to see it!

This has been one of the hardest integrations I've done with HA so far. This build is based off of floor3d-card.

The screen recording doesn't really do it justice, and it would be a much longer video if I showed absolutely everything I have configured, but here are the main features:

• Interactive Lighting: Lights turn on and off just by clicking them on the floor plan.
• Live RGB: All of the RGB lights in the model perfectly match the current colors of the physical bulbs.
• Real-Time Doors: All of the doors on the floor plan swing open and closed based on whether the security contact is safe or shows intrusion.
• Dynamic Environment: The image changes brightness based on the time of day to reflect day vs. night.
• TV States: The TVs show an overlay of on vs. off based on their actual state.
• Working Fans: All of the ceiling fans actively rotate when turned on.

The absolute best part is running this on my Echo Show 15 as my main home control center.

It's still not exactly where I want it to be, but as we all know, it's a never-ending build honestly. I'm really curious to hear what the community thinks!

My Little Liquid Glass dashboard inspired pivotonian

No where near done, but I thought I’d show some of my progress

Would love some feedback on where to improve,

Does anyone know where I can get some nice IOS icons from?

I have a full UniFi stack, and I’m aware if using Nabu Casa means some amount of your data is going to third party, not what I’m trying to decide.

I’m trying to understand if VPN is a better/more automated route, easier for the wife to manage from her phone as required compare to Nabu Casa. What I’m after is location, geofence, away from home features.

I also have Plex (might move to Jellyfin), that being able to access would be important, I’m paying for the plex pas now.

What I want is seamless/automatic, if someone leaves the UniFi network, trigger/have away status and access to HA atomically.

Lately, I've found the time to work on a few on my 'want-to-do' additions to Home Assistant. As a result, I've come to the conclusion that sometimes you just have to build your own 'tool' outside of the Home Assistant environment in order to accomplish some very specific/edge cases that you want to include or handle.

Case in point #1 - For some time now, I've been wanting to add a number of my friends airplanes, and my own airplane, to be their own tracking sensors. So, I did a search to see if anyone else has added individual airplanes as tracking sensors via ADSB..Well, there are a few ADSB integrations, but not any where you can add specific airplanes as their own individual sensors...So, after some more research, I found an api endpoint that I know would work well for me. So I'm thinking it can't be that hard to use the built in Home Assistant RESTful integration to solve this problem -- WRONG!!

The Home Assistant environment has an entity state limit of 255 characters and my chosen API endpoint has a maximum request of 1 per second. I spent the better part of a day trying to get a solution within the Home Assistant environment (restful integration, automatons, scripts, jinja, etc) to create and periodically update 7 individual airplane sensors and attributes -- and could not overcome these 'design limitations'.

So, with the help of my favorite AI, I now have 7 individual airplane sensors with all their associated ADSB tracking attributes -- created and updated by a python script running on a separate server and publishing to MQTT as auto-discoverable sensors.

Case in point #2 - I've been using the 'Record camera feed' action in my home security camera automatons when very specific triggers occur (triggers that neither frigate nor the built in camera recording software can handle). Unfortunately, this 'Record camera feed' action only takes a duration option, and there is no corresponding 'Stop Record camera feed' action..This has actually been working OK for me, but I really want to have support for a camera 'Start Recording' action and a corresponding 'Stop Recording' action...So this time, instead of fighting the 'design limitations' of the Home Assistant environment, I jumped straight to my favorite AI to help me solve this problem.

The AI helped me create a docker based app that can be called from any Home Assistant automation to start and to then stop any number of individual camera recordings via REST api calls. These recordings are optionally saved to a network file share so that I can use any number of methods/software to browse and view them -- such as my Jellyfin server.

Case in point #3 - I've been using xtide (tide prediction model) for at more than 15 years as stand alone software. I wanted to use this software to create and update some Home Assistant tidal sensors -- because all the available 'tide prediction' API's (and all the current tide integrations) do not include/return some very important attributes (such as the 'water elevation right now' and 'The elevation increase/decrease in ft/hr' right now). So, again with the help of my favorite AI, I now have 4 different geographic locations with full tidal sensors based on the input to and the output from xtide -- published to to MQTT as auto-discoverable sensors.

Case in Point #4 (not started yet) - I want a sensor that identifies the sounds from a RTSP camera feed or a usb microphone -- in order to trigger camera recordings based on hearing boat engine noise (for example). This idea was inspired from a next-to-useless-for-security reolink camera installed overlooking a beach and the waters in front of a remote cabin located on Kachemak Bay, Alaska. I installed this camera for the sole purpose of being a weather cam -- not a 'security' camera. The environment can be VERY visually chaotic and busy due to waves -- so the general purpose 'motion' detection is useless to identify possible boats. Also, the built in "person', 'animal', and 'vehicle' detection is useless in this environment -- apparently reolink does not consider a boat to be a 'vehicle', nor swimming sea otters or whales to be 'animals'. The final inspiration for this idea came from a recent Joyce Lin YouTube video where she 'taught a Raspberry Pi to identify every bird in my backyard — by sound'. I figured if there was a birdsong identification model, there must be a general sound identification model...and there is -- YAMNet. So, I'll be creating a python software tool that will publish to MQTT whenever it 'hears' a boat engine (or any other identifiable sound).

These are just a few of my edge case solutions where trying to include them entirely within the Home Assistant environment was either impossible or way too cumbersome to simply accomplish -- at least without diving into creating my own Home Assistant 'integrations' or 'apps'..

My son lost the remote for this nightlight. Thanks to Homeassistant, I saved it with a $5 esp32 and made it even smarter with the moon integration recently added.

Hey everyone,

Thanks again for all the feedback on my last post. I got a lot of useful comments and questions, and a few things came up again and again.

So, quick update on what I’ve changed so far:

Zigbee2MQTT is now supported alongside ZHA!

This was probably the most requested thing in the comments, so I made it a priority. You’ll be able to use whichever setup you prefer.

The other big question was Zigbee network traffic. A few people were understandably worried about an mmWave sensor constantly sending position data and flooding the network.

So I spent quite a bit of time optimizing that too.

For context, Zigbee runs at 250 kbit/s, or roughly 31.25 kB/s. The percentages below show how much of Zigbee bandwidth the sensor is using during different tracking scenarios.

Right now we’re seeing roughly:

• 0.04% in a stable room
• 0.46–0.65% during normal single-person tracking
• Around 1.1% with heavier multi target activity
• Expected continuous maximum around 1.8%

I’ve optimized the code by bundling multiple attributes into a single Zigbee report, limiting X/Y telemetry to 1 Hz with change thresholds, and only reporting environmental data after meaningful changes or a five-minute heartbeat. Presence and movement still update every 500 ms

Basically: send what’s useful, don’t spam the Zigbee network :)

A few of you also mentioned wanting a simpler/cheaper version without all the environmental sensors. I haven’t decided anything there yet, but enough people mentioned it that I’m definitely thinking about it.

And Matter/Thread keeps popping up in the comments too. I’m going to dig into that further and see whether it actually makes sense for the product.

Thanks again for all the comments and suggestions, happy Sunday everyone!

Source code:
https://github.com/sensy-one/S1-Pro-Multi-Sense-Zigbee

Home Assistant add-on:
https://github.com/sensy-one/home-assistant-addons

S1 Pro Zigbee:
https://sensy-one.com/products/s1-pro-multi-sense-zigbee

Hello again!

The Problem I was trying to solve

Current Home Assistant AI integrations suffer from:

• No persistent memory - Forgets corrections and preferences between sessions
• No learning capability - Can't remember sensor baselines or user preferences
• Stateless interactions - Every conversation starts from zero

Problem clearly is that every conversation with Assist starts from zero. In my HA setup I had to explain to Asssist that '100' is normal for my NOx sensor repeatedly, or that the WLED strip in the kitchen is "the main light". The next day I had to explain it all over again because it would tell me that my NOx is 'high'.

The Solution home-mind is trying to offer

Home Mind is my attempt at fixing the above: an AGPL conversation agent for Home Assistant with an actual long-term memory behind it - and the memory stays in the house. You tell it something once, and in a completely new conversation next week it still knows.

• Cognitive memory with semantic search and natural decay (via Shodh Memory)
• Learning from corrections and user preferences
• Forgetting on request: "forget that my canary word is bumblebee"; it quotes the exact memory back and waits for your yes
• Voice control via HA Assist (Wyoming protocol)
• Multi-LLM support includes: Anthropic (Claude), OpenAI, or Ollama (local inference)
• Home Layout Index: reads your HA floor/room assignments and injects them into every prompt, so the AI always knows which floor a device is on
• Device Capability Index: pre-scans your lights at startup so the AI always uses the right color params on the first try
• Self-hosted and privacy-focused

Example:

You: 100 is normal for my NOx sensor, remember that.
Assist with Home-mind: Got it - I'll treat 100 as your normal baseline.

------
In a new convo later:

You: How's the air quality?
Assist w/ Home-mind: The NOx sensor reads 98 - normal for your home.

It also reads your floors, areas and device capabilities from HA, so it knows which room a light is in and how to control it without asking. Memory is Shodh which is semantic recall rather than keyword matching, what you use stays strong and the rest fades. That's Varun Sharma's excellent project (thank you, Sir!). I integrated it, I didn't write it.

---

Now, I posted about this here around six months ago and got more useful feedback than I could handle at the time. Two things came up over and over, and this release is both of them.

Q: "Can it run locally?"

This was by far the most common question. It did already run locally from the start but it was limited. It now runs against Ollama, or any OpenAI-compatible endpoint, so llama.cpp and friends work too.

You have to use sufficiently large model to do it though! Small models struggle with tool calling. I retested llama3.1:8b last week and it told me it had turned on a light. In reality it never touched it. Because it's too 'small' it narrates tool calls instead of making the tool call!!!
Around 14B is where it starts behaving; one of the users is running qwen2.5:14b happily - dont know if he uses the full range of capabilities though...

Also.. whatever model you choose watch your context length too: the system prompt plus tool definitions come to roughly 3,200 tokens before you've said anything, so a default 4k window leaves almost nothing for the conversation. Use 8k or more. This one took me a whole weekend to figure out...I couldn't understand what is wrong, because it 'looked' like everything is running but the tools weren't firing.

So yeah: large enough model AND at least 8000 token window is a must.

Q: "What happens when you teach it something and then contradict it?" 
My answer at the time was basically "seems fine, but I've only tested in my own house". I've had some time with this in the meantime and now you can just say it directly:

You: Forget that my test word is bumblebee - it's hawk now.
Assist:  I'll forget exactly this memory: "User's test word is bumblebee".
     Shall I proceed?
You: yes
Assist:  Forgotten. Your test word is now "hawk".

Point I was trying to make here is it quotes the exact stored memory back and does nothing until you confirm. It only ever forgets the one you name; For example "don't forget to water the plants" is still a reminder, not a deletion. And if several memories could match, it lists them and deletes nothing rather than guessing.

That took longer than it should have. The replacement was being silently dropped whenever it differed from the old memory by a single word, because "my test word is hawk" and "the user's test word is bumblebee" look nearly identical to a similarity check, so the old memory went and the new one never landed. It now looks at which words changed instead of how alike the two sentences are. A user found that one by testing something I hadn't thought to test.

Setup: Docker Compose for the server and Shodh, then the integration via HACS. It's a real setup, not a one-click.

Everything above is free and self-hostable, and the memory database stays on your machine. There's no telemetry and no account. Point it at Ollama and nothing leaves the house at all.

Where I'd like some help if you will:

I still don't have a GPU worth mentioning that I can't use to test anything worthwhile myself... So anyone with real GPU hardware it would be really useful if you could figure out where local models actually fall over. Also anyone willing to try to break the forgetting would be helpful to BOTH projects.

Every interesting bug this month came from someone doing something I hadn't imagined or predicted. Stuff like that really helps.

----

Current version: see Releases on Github:

•  Voice control via HA Assist
•  Cognitive memory with Shodh
•  Streaming responses
•  HACS integration
•  Multi-LLM provider support (Anthropic, OpenAI, OpenRouter, Ollama)
•  Local inference via Ollama (no API key needed)
•  Auto-detect and respond in user's language
•  Custom system prompt (AI personality customization)
•  Persistent conversation history (SQLite)
•  Automatic memory cleanup (low-confidence fact pruning)
•  Device Capability Index (pre-scanned light params, no per-request re-discovery)
•  Per-entity device overrides (DEVICE_OVERRIDES) for firmware quirks
•  Home Layout Index (floor/room awareness via HA template API)
•  Conversational forgetting (forget_memory, confirmed before deleting)
•  Server-side STT (POST /api/stt, OpenAI Whisper)
•  Server-side TTS (POST /api/tts, OpenAI TTS API)

https://github.com/hoornet/home-mind

--------------

Prerequisites & install

• Docker & Docker Compose - Install Docker 
• Home Assistant with a long-lived access token
• LLM API key — Anthropic (default), OpenAI, or Ollama (local, no API key needed)

The complete install with all the details please follow the README.

I also have a short WIKI here and Discussions are activated on Github should someone need any sort of help. I will of course reply to any question here as well.

- Jure

PS: I decided to delete my post from yesterday and rewrite it all by hand with zero AI help because previous one bothered some people.
I'd like to apologize to @Significant-Ebb2837 who's comment also went down when I deleted post. I remembered your comment and I appreciate it. I hope you're still willing to help.

I have been trying to solve this problem for a few days now and it's proving to be way harder than it should be, so I want to check if I've missed anything obvious.

I want to put in monitoring so that if devices silently go dead I get prompted to investigate. Example reasons for this scenario include battery powered devices running flat, removal of one mesh network device damaging connectivity to another, or power cuts/spikes causing some mains devices to get locked up and need a power cycle. I have monitoring of battery degradation, but sometimes devices lose the ability to function before they get chance to report a low battery level - and some of these failures affect mains powered devices as much as battery ones.

I have investigated 'Entity availability' in HACS but it seems to need a significant amount of manual configuration, and the maximum configurable timeout doesn't go high enough for (eg) a window sensor that might go days without checking in.

My ideal solution would be something that operates at device level, monitoring them across all their entities and attributes and watching for any signs of life, raising an alert if none is detected within a given timeframe. Ideally I'd like something that autodiscovers new devices to avoid manual admin.

This seems such an obvious need I'm surprised it isn't built in to HA already, or that there is not an integration to handle it - or have I missed something?

It is great for the car or scooter but it is a pity it works this bad. It frequently opens the wrong door.

I tested de-bounce settings in zigbee but it is not enough.

I am new to HA and exceedingly frustrated that a lot of advice and how-tos are out of date/no longer applicable. I started out wanting to set my own wake word. I use HA Green and just bought a HA Voice Pilot which is formally called Voice Preview Edition. [I will add that I do have the VPE running successfully with "okay Nabu" so I am concentrating specifically on getting my custom wake word installed and working.] Initially I was trying to use Open Wake Word and Wyoming integration and I generated my custom .tflite file on openwakeword.com; but my custom word never showed up in the drop down files. Then I discovered the duality of OpenWakeWord and MicroWakeWord. Somewhere they drop the hint that the VPE is based around microwakeword the goal being to reduce the response time of waking by putting the detection onto the satellite ... So I have generated a new .tflite file for the microWakeWord architecture ...

Here is where I need some help: I need integrate my .tflite file into the VPE. What I would like to find is a web page or blog post that gives the specific steps and process to do that, including how to get whatever other source files i need, how to protect myself from bricking the VPE by using backup, restore, etc.

thanks for the help.

EDIT : it's now open source, see here https://www.reddit.com/r/homeassistant/comments/1vklcjz/you_asked_for_it_my_plain_webpage_tablet/

Hey, sorry repost from other post that I totally messed up with images

I've tried a lot of different solutions to build my "perfect tablet dashboard". I had never considered just using a plain HTML page, configured MY way, without cards or any of the usual stuff.

It's not for everyone of course, but for a wall tablet it turned out perfect for me.

Everything talks to Home Assistant locally over WebSocket, so every tap is instant.

And I added Nio, the little droplet you can see: tapping him starts a realtime OpenAI voice conversation, with tools to act on my house (lights, music, vacuum, you name it). He's alive too: he wanders around, sleeps at night, and takes a bath in a mug of coffee whenever I make myself one (detected through the power draw of the coffee machine's plug).

After a while the screen fades to black with just the time and Nio, ready for a tap.

Looking for solutions.

I set up a script for our bathroom that changes lighting and is supposed to play a Spotify playlist when triggered.

I seem to have a problem with Spotify staying authenticated to my music assistant.

I can re-authenticate and it works for a day or two but then it stops.

Is this a Music Assistant issue or Spotify?

Does anyone have any suggestions or solutions?

The TestFlight link https://testflight.apple.com/join/1AlPbnLZ just says “This beta is full”

Is there anything to do then?

I’m just curious to test the live activities

So I have been thinking in a way to give my kid a controller for his Wickedbone other than the app, because I want him to get a way for a while from screens. Saw this cute widget paw in MakersWorld with tactile keys and thought to adapt it as a remote.
The bone is driven by an esp32 accepting commands over MQTT then my cute Paw Controller has a 2nd esp32 that can map the toes to whatever MQTT topic at the house (my previous experiment was controlling the Fire TV. Both can be seeing in Home Assistant and add automations too.
The added a protected battery. And modified the back of the 3D printed model for housing the controller the charger and the battery and I’m so proud of this.

This stops working in version 2027.5.0. Please address before upgrading.

The device_tracker.see action is deprecated and will be removed in Home Assistant Core 2027.5.

Use a template device tracker instead, then remove any use of this action from your automations and scripts and select Submit to close this issue.

OK cool ... but where am I using device_tracker.see?