You can flash a pre-compiled firmware or build it from source yourself. More information and instructions are available here: https://codeberg.org/Joseph-DiGiovanni/esphome-litter-robot/

Will hopefully be upstreamed to ESPHome soon

Hey everyone,

Looking for some serious advice from the community. Last night at 3 AM here in Glasgow, UK, someone broke into our house. They stole my bike from the garden, broke inside, stayed in the house for 20 minutes, ate some of our food, grabbed my iPad, and left.

I currently have Reolink cameras (a video doorbell and an Argus 4 Pro in the backyard) integrated into my Home Assistant setup. The crazy part? The cameras did absolutely nothing to deter him. He didn't even bother wearing a mask, gloves, or a hat. He just didn't care that he was being recorded.

I realized today that relying on silent cameras isn't enough. I need to drastically increase the active security of my house to physically scare off intruders or alert me immediately so I can act.

My Current Setup:

• Home Assistant running on a Raspberry Pi 5
• SkyConnect (ZBT-2) running Zigbee
• Reolink Doorbell (Front) & Reolink Argus 4 Pro (Back)
• A mix of IKEA and Sonoff smart plugs/lights

Where I need your help: I am trying to decide between building a fully DIY alarm system through Home Assistant (using Alarmo, Sonoff SNZB-04P door sensors, and Zigbee sirens) OR just buying a ready-made professional solution (like Ring Alarm or Ajax) and integrating it into HA.

1. DIY vs Professional: For those of you who have experienced a break-in, do you trust your Home Assistant/Alarmo setup enough to rely on it as your primary life-safety alarm, or do you highly recommend going with an off-the-shelf system with cellular backup?
2. Sirens/Deterrents: What is the loudest, most aggressive outdoor/indoor siren that integrates well with HA? I want something that will absolutely deafen them if a door opens at 3 AM.
3. Any other advice? Glass break sensors? Specific outdoor security lighting?

I am fully open to any and all suggestions. I just want to make my house as safe as possible going forward. Thanks in advance.

Went to IKEA yesterday and lucked out and found two zigbee device types for 4.99 each. Picked up a couple of Matter devices to play around with.

is there a way to track location in home assistant similar to google maps timeline where i can see everywhere I went including the paths I took?

I have location enabled to the point where I can see my current location on a map including the history of every GPS update (which I think happens every 5-15 minutes if coordinates change? I'm not too sure how the updating works) but I want to be able to see the entire history on a map instead.

I'm looking to start playing with Matter but am rapidly getting more confused as I read more.

Here is what I'm unclear on:

1. Do I need a Matter hub or not? I have a dedicated HA server. I thought I read that I need a hub to connect devices to, and then HA looks at the hub to discover devices. I was reading a different thread though that was saying I could just get a Matter antenna?

2. Do I need a thread router? I think I read that this is to extend device control into other assistant ecosystems, such as Apple Home, Google Home, etc..

3. I'm a bit confused about Matter vs zigbee/zwave. I understand they are different languages, but can zigbee or zwave devices connect and talk to the matter hub?

4. I'm a bit unclear where to begin hardware and associated integration wise. For example, if I get a Tapo device, do I set it up in Tapo, and then in Matter? Just Matter? Other?

Any pointers or recommended kindergarten level guides would be greatly appreciated

built three new widgets for magic frame. media player, status cards and rss.

status cards are my favourite, they pop up while something is running (car charging, 3d printer, kids toniebox) with the devices picture entity and live values, and disappear again when its done.

also did some other stuff, live preview while youre editing a widget, atmospheric backgrounds for the weather widget and a qr code widget.

thanks to everyone for the feedback, most of this came from stuff people asked for.

self hosted, vibe coded - https://github.com/jeremiaa/magic-frame

I got a lot of the same question on my "Okay Nabu" post, so I thought I'd document my journey and setup here. Hope this helps somebody else get rid of the evil witch. OpenAI is temporary or not needed if you have better hardware than I do.

1. Replaced all my Etekcity outlet boxes and switches with a local Z-Wave hub and controller. I used Z-Box, there are many
2. Got both Alexa and Home Assistant talking to Z-Wave
3. Set up Voice Preview in parallel to Alexa (so we could still turn on the lights reliably)
4. Converted everything else to Z-Wave over about a year. This was expensive vs. cheapo IoT stuff. New switches, sensors, plugs, thermostats pulled it all into HA. Pruned as much IoT as I could.
5. Added separate turn-on and turn-off automations for every room. HA's interpreter catches the command first, not the LLM. Makes it consistent.
6. Tried fully local voice stack: Whisper STT + Piper TTS + LM Studio (Gemma) on M2 Mac Mini. Performance was horrible and it made mistakes all the time.
7. Spent way too long to get weather working as HA sensors using OpenWeather One Call REST. Sensor holds the forecast raw data, and automations send it through LLM + TTS on request.
8. Suffered with both systems in place, then unplugged the Voice Previews for 6 months. My local STT, TTS, and LLM were too slow for this to be usable and we relapsed into Alexa
9. Alexa forced me into Alexa+ twice and made me mad. I dusted off the Voice Previews and used OpenAI API keys for LLM, STT and TTS to be rid of the Amazon bullshit. I know cloud AI sucks, but it means I can use this now instead of waiting
10. Unplugged all my Echos and decided to tough it out. Surprisingly, it all pretty much worked.
11. Set up Jellyfin, then Music Assistant. It all works.
12. Added an ATOM remote station, ordered a few more Voice Previews, and hooked one to a soundbar over AUX for decent audio.

At this point we're at major feature parity with how we personally used Alexa. I miss my smart clock, multiroom synced audio, and synced timer control but I do not miss Alexa+, "By the way..." or getting advertised at constantly. Worth it.

Next Steps: Try STT on my Mac running Wyoming, move to local LLMs once I can upgrade my hardware.

I know many of you are local-or-nothing and that's great! I am moving in that direction while using the solution that meets most of my needs right now. I wanted Alexa gone immediately, and I'll keep working towards increased privacy.

P.S. This setup right now only costs me $0.35/week in API usage and it's fast, lights are faster than Alexa, weather is a little slower.

I'm running HAOS and Jellyfin on proxmox on an old Ryzen desktop. I have 3x Voice PE units, 1x M5Stack Atom Echo remote station, and I'm using a soundbar. I have a Z-Box hub and a YoLink sensor hub. The most expensive part was replacing the outlets since Z-Wave stuff can be 3-5x the price of IoT. For now, I'm using a personal OpenAI account with a prepaid API balance (pay-per-use, no subscription). I use gpt-5.4-mini for the LLM.

I'm not sure I'd recommend Z-Box over a USB Z-Wave controller, my server is just behind some walls that blocked a lot of the radio signals so I needed the transmitter in another room. The YoLink is also more IoT than I like but the sensors are great.

Hope that helps somebody!

I have 2 identical ESP32s acting as BT proxies.
BT1 - next to the door out to the garage on the east side of the house
BT2 - 2nd floor hallway, slightly West of center

When I look at the BT Visualization in HA it shows the BT2 connecting to the Switchbot lock that's on the door to the garage. The BT1 is sitting literally a few feet away in the same room with no obstructions. The BT2 that's connecting is on a different floor, behind 3 walls, an electric dryer, a ton of plumbing, etc. Why?

Similarly, the BT1 on the 1st floor will often connect to the Switchbot Hub2 and roller shades on the 2nd floor, on the complete other side of the house, when the BT2 is on the 2nd floor right outside the bedroom where the Hub and shades are. Why?

What's more confusing is that sometimes if I click the "Active connections," it shows the BT1 as connected to the Lock that's next to it, but the visualization shows that the BT2 is connected. What's going on?

I've reached the point where I'm looking for those small automations that make a real difference not the crazy over the top projects.

Always looking for new ideas..

This is all we used Alexa for. After the second forced "upgrade" to Alexa+ I exiled them. It's been great so far!

I did cave and used chatGPT for the assistant, STT, and TTS because it's way faster than my LMStudio or Ollama setup, but once I have better hardware chatGPT gets to join Alexa in Narnia. At least "By the way..." is gone!

Edit: A few people asked about my setup and how I got here, so I made a post detailing it here https://www.reddit.com/r/homeassistant/comments/1v3k7ma/12_steps_i_used_to_get_rid_of_alexa/

Just started getting this. There are no new updates and nothing on the system has changed. Says I need to update to latest version. Thanks

call to https://gl-us-api.ecovacs.com/v1/private/us/EN/C7QXDODL/global_e/1.6.3/google_play/1/user/login failed with {'code': '1013', 'msg': 'Please update to the latest version to continue.', 'time': 1784208218200, 'data': None, 'success': False}

Hey all you smart people out there. I'm delving into the voice side of things for the first time. Trying as many of you are to get away from Alexa. One of the things I use if for regularly in the kitchen is baking conversions. Things like:
"How many grams is a cup of flour."
"How many tablespoons is in a quarter-cup."

I expected to see something created for this already since I've been using those kind of features on Alexa since before Alexa+, but so far everything I've found has just been telling me to plug it into an LLM. I expect I'll probably go down that rabbit hole at some point, but my question is this. Are there any existing integrations or projects that can do this without an LLM or is it practical to build? Or should I just wait until I can get AI integrated how I'd like?

Hi everyone! I wanted to share the main dashboard of my smart home.

Home Assistant runs on Ubuntu 24.04 installed on an HP t630 Thin Client. The dashboard is permanently displayed on an inexpensive 16-inch Chinese monitor.

Most cards were built manually with custom:button-card, JavaScript, and CSS. My goal was not just to display a lot of numbers, but to create an interface that remains readable from across the room.

Here is a quick overview, starting from the top left.

1. Core device monitoring

The first section contains three system monitoring cards:

• Ubuntu Server, running Home Assistant: CPU load and temperature, RAM usage, disk usage, and the last boot time.
• MikroTik hAP ax²: CPU load and temperature, memory, storage usage, and uptime.
• Mac mini M4: CPU load, CPU/GPU temperatures, RAM usage, disk usage, and uptime.

I only included metrics that help me quickly understand the overall health of my home infrastructure.

2. MikroTik and internet monitoring

The larger network card displays current download and upload speeds, a traffic history graph, packet loss, current and average latency, jitter, and the route to an external host.

A custom service running on Ubuntu continuously executes MTR, short for My Traceroute, to 1.1.1.1. It collects statistics for the last hour and exposes the results to Home Assistant.

This helps distinguish between problems inside my network, the ISP network, a transit node, or the destination. Notifications are based on actual end-to-end packet loss rather than ICMP rate limiting on an intermediate router.

Home Assistant sends an alert when the connection becomes genuinely unstable.

3. Family phones

These cards show each phone’s battery level, charging status, model, and approximate location.

There is also a compact family section with battery indicators and quick access to location information.

4. Camera status

Two small cards show whether each camera is online, its Wi-Fi signal in dBm, and its LIVE status.

One camera also records continuously to a separate drive. Recordings are split into manageable files, and older footage is removed automatically.

5. Inverter and battery

This card monitors an Afore inverter and a Dyness LiFePO4 battery. It displays:

• grid voltage, current, and frequency;
• AC and DC temperatures;
• current household load;
• battery state of charge, voltage, current, and temperature;
• daily household consumption;
• energy imported from the grid.

The voltage scale changes according to the current value, while the power paths have subtle animations.

Separate automations send notifications when grid voltage remains below or above configured limits.

6. Lighting

At the bottom are three compact controls:

• a smart corridor light triggered by motion at night;
• an LED strip indicating whether an air-raid alert is active;
• TV ambient lighting.

Each button reflects the actual color currently used by the corresponding light.

7. Clock

Just a large clock. Nothing particularly clever here, but it is readable from the other side of the room.

8. Notification card

This compact widget shows the latest Home Assistant notification, including doors, sensors, water leaks, air quality, air-raid alerts, and other events.

Messages are automatically classified and receive different colors and icons depending on their category and severity.

9. Air-raid alert and threat radar

This is the most complicated and, unfortunately, the most useful part of the dashboard.

Special “thanks” to our neighbors in Russia. Without their missiles and drones, this would never have become the most advanced widget in my smart home. I would much rather have spent this time building something more cheerful, but this is the reality we live in.

The card uses an air-raid alert integration along with separate helper entities that store the start and end time of each alert and calculate its duration.

A custom script also processes data from NEPTUN. It receives a list of active aerial targets and determines:

• target type;
• nearest locality;
• approximate distance from home;
• position and reported heading;
• total number of active targets;
• number of UAVs, FPV drones, missiles, guided bombs, reconnaissance drones, aircraft, and other threats.

I created a separate transparent PNG icon for every target type, including UAVs, FPV drones, reconnaissance drones, missiles, ballistic threats, guided aerial bombs, and aircraft.

When an air-raid alert is active and the nearest target is within 100 km, the regular illustration is replaced by an animated radar. It displays the approximate direction, distance, and position of nearby targets.

The radar is calibrated to match the physical orientation of the wall-mounted monitor, so a quick glance gives me an approximate idea of which side the threat is coming from.

When a target crosses the 100, 50, or 20 km threshold, Home Assistant sends critical mobile and Telegram notifications together with a fresh screenshot of the alert widget.

This is only an additional visualization. It does not replace official air-raid alerts, government warnings, or instructions from the authorities.

10. Weather

The current outdoor temperature comes from my own sensor. The remaining data comes from a weather station located approximately 50 meters from the building.

The card displays current temperature, a multi-day forecast, humidity, atmospheric pressure, wind speed, AQI, sunrise, and sunset.

11. Indoor climate

Separate room and kitchen cards show temperature and humidity.

The thermometer image changes depending on the temperature range: cold, comfortable, or hot.

12. Sleep tracking

Sleep cards use data from Garmin and show sleep duration, score, and overall quality for family members.

Colors change automatically according to the result.

13. Spotify

The music card displays album artwork, track name, artist, playback progress, and basic controls.

14. Telegram air-threat monitoring

Another custom script parses messages from four public Telegram channels reporting aerial threats.

The widget displays the latest relevant message. If the text indicates a direct threat to my area, the card flashes red, Home Assistant sends notifications, and a TTS warning is played through the speakers.

This is only my main dashboard. I also have separate pages for cameras, energy, alert maps, device controls, and technical information, but they are used less often and are not as polished yet.

Voice announcements are also configured for:

• the beginning and end of air-raid alerts;
• nearby aerial threats;
• doors opening and closing;
• water leaks;
• sensor activity;
• abnormal grid voltage;
• unstable internet connectivity.

The project is constantly evolving. I add things, remove things, and occasionally spend half an evening moving one element by three pixels because it looks wrong on the smaller screen.

I would appreciate feedback, constructive criticism, and ideas for future improvements.

Please do not roast me too hard. I have only been using Home Assistant for a few months.

Since some people on my last post asked for the setup details, here is a write up of how exactly my HA LLM + STT is set up.

I run Gemma 4 12B Q8_0 locally on an NVIDIA RTX 2000 Ada Generation GPU with exactly 16 GB VRAM. The A2000 Ada was selected speciffifally for its very low stand by power usage of around 5W and its also low inference power draw of 70W. Its performance is sufficient for small models like gemma 4 12B in my usecase.

The same Gemma instance handles normal LLM usage and German Home Assistant Voice Assist transcription:

llama.cpp setup

I use the CUDA llama.cpp server and configure models through the models.ini file instead of maintaining a large startup command.

The active model artifacts are all from Unsloth's `unsloth/gemma-4-12b-it-GGUF` release family:

• Main model: gemma-4-12b-it-Q8_0.gguf
• Multimodal projector: mmproj-gemma-4-12B-it-bf16.gguf
• MTP draft model: mtp-gemma-4-12b-it.gguf

The `mmproj` file is mandatory. It enables Gemma's multimodal input path, including audio transcription. Without it, this is only a text model.

My active preset:

[Gemma4-12B-Q8_0]
model = /models/gemma-4-12b-it-Q8_0.gguf
mmproj = /models/mmproj/mmproj-gemma-4-12B-it-bf16.gguf
model-draft = /models/mtp-gemma-4-12b-it.gguf

spec-type = draft-mtp
spec-draft-n-max = 3

n-gpu-layers = 999
flash-attn = on
parallel = 1
batch-size = 256
ubatch-size = 256
cache-type-k = q8_0
cache-type-v = q8_0

ctx-size = 140000
jinja = true
reasoning = off

Performance on 16 GB VRAM

This configuration supports up to 140,000 tokens of context in my setup. Your actual limit depends on driver, llama.cpp version, request size, and other models loaded on the GPU, so monitor VRAM usage yourself.

For most tasks, 140k context is okay.

The settings that matter most are:

- Full GPU offload through "n-gpu-layers = 999"
- Flash Attention enabled
- Q8 KV cache
- One parallel model request with "parallel = 1"
- MTP speculative decoding

The matching Unsloth MTP draft model made one of the biggest practical differences to response speed. llama.cpp can validate multiple predicted tokens at once instead of generating every token sequentially. This noticeably improves normal LLM response latency even on a relatifly weak GPU.

Disable reasoning

This is essential when the same Gemma model is also used for STT:

reasoning = off

It must be disabled in the llama.cpp "models.ini" preset itself. Otherwise reasoning is also enabled for transcription requests.

Without this setting, Gemma can generate unnecessary reasoning or explanation tokens around short voice commands. That makes STT slower and less reliable.

Googles Update for Gemma

Gemma is very capable across languages. German works well for both normal conversation and speech transcription in my experience, but for tool calling it had some problmes in the past.

Google recently released an updated official Gemma chat template. The active Unsloth Q8_0 GGUF now also includes this update, and llama.cpp uses the embedded template through:

jinja = true

This matters for tool calling. The updated official template formats tool definitions and tool responses more reliably than older versions.

I would not blindly add an old external Jinja template file. Make sure your GGUF contains the current official template and that llama.cpp uses the embedded version.

Home Assistant STT

Home Assistant uses the Wyoming protocol, while llama.cpp provides an OpenAI-compatible transcription endpoint.

I connect both with: ghcr.io/roryeckel/wyoming_openai

The request path is:

Home Assistant -> Wyoming OpenAI -> llama.cpp /v1/audio/transcriptions

The Wyoming container does not need GPU access.

Relevant container settings:

- Restart policy: unless-stopped
- WYOMING_LANGUAGES=de
- STT_OPENAI_URL=http://<llama-server>:<port>/v1
- STT_BACKEND=OPENAI
- STT_TEMPERATURE=0

The most important STT setting is:

STT_TEMPERATURE=0

My normal LLM profile uses more creative sampling, but STT should be deterministic. Without this per-request override, Gemma sometimes transcribed correctly and then continued with a translation, explanation, or answer to the spoken question.

I use this transcription prompt:

Task: German speech recognition. Return only the spoken German text verbatim. Do not translate, explain, summarize, answer, format, or add labels. If there is no intelligible speech, return an empty response.

The prompt is passed as the OpenAI transcription "prompt" field, not as a normal chat system prompt.

Home Assistant setup

1. Add the Wyoming Protocol integration.

2. Point it to the Wyoming-OpenAI container.

3. Select the discovered Gemma STT engine in the Assist pipeline.

4. Set the Assist pipeline language to German.

Avergate Timings are:

0,8 Sec for STT
2-3 Sec for LLM response with (multi) tool use

TTS

This setup has no local TTS.

Gemma does not generate speech in this deployment, and a 16 GB GPU running Gemma 4 12B Q8_0 has no meaningful VRAM left for a separate high-quality local TTS model.

I would keep TTS separate and use ElevenLabs for natural German voices. That keeps the GPU focused on Gemma for LLM and STT while providing better speech output quality.

Summary

A 16 GB GPU is enough for Gemma 4 12B Q8_0 as a local LLM and Home Assistant STT backend when configured carefully:

- NVIDIA RTX 2000 Ada Generation with 16 GB VRAM
- Unsloth Gemma 4 12B Q8_0 GGUF
- Matching Unsloth BF16 `mmproj`
- Matching Unsloth MTP draft model
- Full GPU offload
- Flash Attention
- Up to 140k context
- MTP speculative decoding
- One parallel request
- Current embedded official chat template
- `reasoning = off`
- `STT_TEMPERATURE = 0`

Sorry for the long post. AI was used for translation.

Original issue:
https://forums.wyze.com/t/api-certificate-issue-breaks-homeassistant-please-fix-your-cert/343728
https://forums.wyze.com/t/certificate-failure-for-rest-api-endpoints/343425
https://forums.wyze.com/t/api-certificate-issue-breaks-homeassistant-please-fix-your-cert/343728/22
https://www.reddit.com/r/wyzecam/comments/1um4q92/apiwyzecamcom_serving_certificate_chained_to/

The Problem: If you recently updated Home Assistant and your Wyze integration suddenly broke with an SSLCertVerificationError or ClientConnectorCertificateError in your logs, you aren't alone. It will also show up in the UI as errorCode: 1000 (Invalid Credentials) because the integration can't securely reach the authentication server.

The root cause is that Wyze's API (api.wyzecam.com) is using an older DigiCert Global Root G1 certificate. Newer Python environments (specifically Python 3.14 inside recent Home Assistant Docker images) no longer trust this certificate by default, causing the secure handshake to fail.

There is a fix for people with HAOS with HACS "Additional CA" program, but that fix doesn't work for docker containers. See comments for that fix if you have HAOS.

Why the HACS "Additional CA" fix doesn't work here: You might see workarounds suggesting you install the "Additional CA" HACS integration. While that works for integrations using the synchronous requests library (which uses certifi), the Wyze custom component uses aiohttp. aiohttp bypasses certifi entirely and uses Python's native ssl module, so it completely ignores the HACS workaround.

The Temporary Fix (For Docker Users): To get around this, we can temporarily tell aiohttp to ignore SSL verification strictly for the Wyze authentication library.

Run this command directly on your Docker host terminal MAKING SURE TO CHANGE 'HA' IN THE COMMAND TO YOUR HA CONTAINER NAME to patch the Python file inside your running Home Assistant container (replace ha with your actual Home Assistant container name if it's different):

Bash

sudo docker exec -it ha sed -i 's/ttl_dns_cache=(30 \* 60)/ttl_dns_cache=(30 \* 60), ssl=False/g' /usr/local/lib/python3.14/site-packages/wyzeapy/wyze_auth_lib.py

Then, restart your container, AGAIN, CHANGING YOUR CONTAINER NAME FROM HA TO WHATEVER YOURS IS:

Bash

sudo docker restart ha

Once Home Assistant boots back up, go to Settings -> Devices & Services and re-authenticate your Wyze integration. It should log in instantly.

IMPORTANT DISCLAIMERS & WARNINGS

1. This is a TEMPORARY band-aid. Setting ssl=False removes encryption verification. Do not leave this permanently.
2. It will not survive updates. Because we are directly modifying a file inside the container, the next time you update or recreate your Home Assistant image, this file will be overwritten and the error will return.
3. When to remove: Once Wyze updates their API certificates or the integration maintainer releases a native patch, you will no longer need this. Pulling the latest Home Assistant container image will automatically wipe this workaround clean.

Noticed a few references to this 'local' integration for Home Connect, but it's not had updates since 2025 and the owner seems non-responsive. https://github.com/chris-mc1/homeconnect_local_hass

Then found this which is a fork from that repo which is more active and has a number of improvements

https://github.com/vemboy200/homeconnect_local_hass

For those who haven't already spotted it

Looking around amazon for different pressure pad sensors, but none of them inspire much confidence. Either terrible spelling mistakes in the ads or proprietary stuff I don't want to work around.

Ideally I'd like to get two of them, one for my couch and one for my bed so I can trigger automations.

I'm currently doing Matter over wifi, as well as using a Tapo Hub. All my smart plugs and sensors are Tapo.

Does anyone know of any pressure pad sensors worth buying that will work with what I currently own? If I have to buy a zigbee antenna, I will, but I'd prefer not to.

Thanks for your help! Also, if you have a unique/cool use case for a pressure pad, let me know down below. For now I'm only planning on using it for basic morning/night routines.

Setup: HA on Proxmox, Tapo Hub, Tapo smart plugs, Tapo temperature and humidity sensor.

Hello i bought a sonnoff hydro duo valve and have it paired through z2m. I have basic knowledge on z2m and home assistant. Im have a schedule as a automation that does each valve on different days, like odds and even days. Im trying to set up rain delays but cant figure it out. Can anyone give me a blueprint or help me set it up?

Im on the latest version of HA on HAOS on promox.

Sorry if this is obvious, but I feel like I'm losing my mind. In previous versions of HA, I could add conditions based on offsetting sunrise or sunset, but now it all appears to be single-choice picks of Sun Is Up, Sun Is Down, It Is Night, and so on with no customization. How do I get this back? Old automations with the offset still work. Thanks in advance!

I've been running Whisper locally for STT and it's been pretty solid once I got the audio pipeline sorted. The wake word side has been a lot harder though. I tried openWakeWord, but with the TV on I was getting too many false triggers. Training a custom model helped, but it's definitely not plug-and-play, especially on lower-powered hardware.

The other issue is latency. Even with Whisper on a decent GPU, the delay is just long enough that people in my house stopped using voice and went back to their phones. Has anyone found a setup that actually feels fast and reliable for everyday use? What hardware are you using for wake word detection, and are you using a pretrained model or a custom one?