Opening the app for the first time on my Fairphone 5 (listed as unsupported) actually crashed the OS, but after that it seems to be working ok.
Closing out of the in-app gallery causes the app to crash. But that can easily be worked around by using some other gallery app.
I’ll be testing it for a bit to see how it fares against other HDR methods…
I am looking at buying a G1 or maybe even a G2 but I haven’t heard of this screen “issue” yet.
Could you please elaborate on that or post a link to someone who already did? (A quick search mostly turned up replacement parts without much explanation)
And please don’t understand this the wrong way.
Ibis seems like a really cool project but with it being roughly half a year old me and many other people here simply have never heard of it before.
Including even a single short sentence describing what Ibis is in this and future posts helps us find projects that we care about more easily.
And we obviously care about Rust projects, otherwise none of us would be here.
Ibis is a federated online encyclopedia similar to Wikipedia.
This should be the first sentence of the post body.
Why not set up backups for the Proxmox VM and be done with it?
Also makes it easy to add offsite backups via the Proxmox Backup Server in the future.
Always mocking Dr. Daniel Jackson. Poor guy
Additional information regarding Home Assistant:
The sun component (which should be enabled by default) already computes the sun position for you.
Elevation and azimuth are available as standalone sensors sensor.sun_solar_azimuth (might be disabled by default) or as attributes on the sun.sun entity.
Not an expert but these systems are fairly self-contained and robust. A few things that can be checked easily is that the fan spins, the radiator is free of debris and some compressors might have a sight glass for the oil level.
Any other checks regarding performance of the system, leaks and refrigerant level require you to perform a full refrigerant discharge and recharge. That takes special equipment and some time so no one in their right mind would do that for free, unless they can then force/guide you into some kind of upsell situation.
Larger systems might have some kind of oil filter/catch-can that you might be able to check easily but I’m not too sure on that.
After all heat pumps are just plain old A/C units with a reversible cycle.
I don’t have any experience with it but this might do something along those lines(?):
https://esphome.io/components/binary_sensor/ble_presence.html
Seems like you can just add it to one or more of your existing esphome devices.
Out of curiosity I’ve let it rate Low<-Tech Magazine, a website run on an ARM SBC powered exclusively with off-grid solar power, and that only achieves 87% / A.
If you have such a system up and running already you could try to modify it before ripping it out and starting from scratch.
Borrowing an idea from the machine learning approach you could additionally take the difference in average outside temperature yesterday and the average forecasted outside temperature today. Then multiply that by a weight (the machine learning approach would find this value for you but a single weight can also be found by hand) and subtract it from the target temperature before the division step discussed previously. Effectively saying “you don’t need to heat as much today since it will be a little warmer”.
I fear that’s about all you can do with this approach without massively overcomplicating things.
This is effectively what a thermostat does.
The problem is that the controller won’t know how well insulated each room is, how cold it is outside (including wind speed), which doors and windows are open and when, what people or devices are doing in each room.
The way thermostats solve this is by creating a closed loop where they react to how the room reacts to their actions.
Depending on how your heaters work you’ll likely need some dynamic component to react to these unforeseen changes unless you can live with the temperature being very unstable.
To get a rough idea of how long the heaters will have to run you can look at each room in for the last n days and see if the heater’s runtime was long enough to (on average) hold your target temperature. Dividing the average temperature with the target temperature will give you an idea whether they were on for too long or too short. (If the heaters have thermostats you’ll likely need to subtract a small amount from that value so that it will settle at the minimum required heating time)
If that value is close to 1.0 you know that on those days the heating time was just about perfect.
Once that is the case you can take the previous days heating time and divide it up over the cheapest hours. The smaller of a value n you choose the more reactive the system will be but it will also get a little more unstable. Depending on your house and climate this system described here might simply be unsuitable for you because it takes too long to react to changes.
There are many other ways to approach this very interesting problem. You could for example try to create a more accurate model incorporating weather and other data with machine learning. That way it could even do rudimentary forecasting.
* $400 / yr
You can use their online web-editor (similar to OverLeaf for LaTeX) or download the open-source engine and run it locally (there are extensions available for many text editors).
Compared to LaTeX I find it much more comfortable to work with. It comes with sane, modern defaults and doesn’t need any plugins just to generate a (localized) bibliography or include links.
Since Typst is very young compared to LaTeX I’m sure that there are numerous docs / workflows that can’t be reproduced at the moment but if you don’t need some special feature I’d recommend giving it a shot.
The development of Piper is being driven by the Home Assistant Project. That probably makes it one of the larger OSS TTS projects. Hope may not be lost yet ;)
Seeing these little IT gems all over Lemmy always makes me smirk :)
I started out with WireGuard. As you said its a little finicky to get the config to work but after that it was great.
As long as it was just my devices this was fine and simple but as soon as you expand this service to family members or friends (including not-so-technical people) it gets too annoying to manually deal with the configs.
And that’s where Tailscale / Headscale comes in to save the day because now your workload as the admin is reduced to pointing their apps to the right server and having them enter their username and password.
One sensor should be enough. I believe they usually mount onto the inside of the window facing outwards so that lights and movement in the room don’t influence it.
The simplest way of solving this would be with technically four separate automations. However you can place them all in the same HA Automation using multiple triggers and trigger IDs. (Or have one for the blinds and one for the lights with two triggers each)
I’m going to assume the blinds are somewhat light translucent.
For the blinds use a numeric trigger that fires if the lux value is over some threshold for let’s say 10 minutes. That way it won’t trigger for every tiny cloud. When triggered lower the blinds.
Add another numeric trigger for moving back up when the lux value is under some threshold for 10 mins. Test to make sure that lowering or raising the blinds doesn’t darken or lighten the room enough to immediately have it trigger the other trigger. If it does then increase the difference between the two thresholds.
Copy the same procedure for the lights. The timer can be shorter here, maybe try 1 minute. Make sure that the thresholds are low enough as otherwise lowering the blinds would immediately turn the lights on. I would suggest first tuning the blind triggers and then tuning the light thresholds to your liking.
If you can’t set the light thresholds low enough so that the blinds don’t interfere with them you’ll need a somewhat smarter automation but I’d try the easy way first.
Mostly its really fucking expensive. Usual applications are central control of heating and window blinds in large office buildings.
Part of what drives the price is that ideally it’s all hard wired.