What do you think makes the combat system of Divinity 2 better? I usually hear the opposite.

Damn, how many tries did that take and how high was your adrenaline level during that playthrough?

Curious about the quant tho.

Q8 from unsloth.

Something like Qwen3.5-122b

My go to model for knowledge. Definitely much faster at Q5 but it lacks the tool calling quality of the Qwen3.6 models. Really hoping we see a Qwen3.6-122b soon…

About 200 t/s prompt processing and 10-20 t/s with MTP.

Greatly depends on the task, predictable things like code generates at 18-20 t/s. Creative writing more like 10-17 t/s.

Yes, I got a Strix Halo machine before the RAM price hike and use it to run all my ML stuff on it.

Currently using llama-swap with llama.cpp/ComfyUI and opencode/Open WebUI as frontend.

I’m running Qwen3.6-27b, Voxtral Mini 4b, Piper and Qwen Image. Also, some embedding and reranking models.

I use them for:

• Tagging and classification of my documents in Paperless
• Home Assistant (voice assistant)
• Translations (both text and image)
• Transcriptions
• Some light coding and debugging
• Avatar/Backdrop generation for DnD sessions

If you have trouble with outgoing mails, you can use a hybrid approach.

Receive mails directly to your server but use a mail service to relay your outgoing mails. Configuration for that is very simple in mailcow and there are a few dozen (free) transactional email providers (e.g. Scaleway).

That way you can keep receiving your mails privately and only have to give up some privacy when sending mails.

According to their now deleted Reddit account, it will use a custom Proton build on Intel and Zen 4 CPUs. For every other CPU you will need a kernel module.

You should be able to just stop Jellyfin, drag the new version into your Application folder and start it again.

How did you install Jellyfin?

As a side note, Qwen3.6-27B is much more capable than Qwen3.6-35B, even though it is much slower.

https://huggingface.co/unsloth/Qwen3.6-27B-GGUF

For coding tasks where you don’t mind waiting, you should be able to barely squeeze in the 8-bit quantized version with 32 GB RAM + 8 GB VRAM and have a pretty competent local model. 4-bit quants work but they have issues with complex tool calls.

If you use the MTP branch of llama.cpp (and a suitable model) you can even double or triple your token generation speed: https://github.com/ggml-org/llama.cpp/pull/22673

For easier tasks, disable reasoning for instant responses.

That looks pretty good. Looks like Portainer is getting replaced this weekend.

Do you actually train the LLM or use RAG? I have been looking for a local LLM + Wikipedia RAG solution for a while now.

For now I just have kiwix-serve + searxng doing a simple search but the Kiwix search is…questionable.

I wrote an application which runs on my server and monitors my favorites on Tidal/Deezer/Qobuz. It downloads them in bulk whenever I have a premium account with one of them. Usually I purchase a month of premium every few months, at which point I get nice clean FLACs for local use.

The FLACs are moved to Jellyfin and I stream them using Finamp, which also supports transcoding, so I keep 128 kbps Opus files for offline playback and stream the raw FLAC files when bandwidth is no concern.

I have amassed a huge music library over the last decades, so even if all streaming websites go under tomorrow, I have enough music locally to last me a lifetime.

Miss it when tech updates were good.

Open source got your back.

Still excited every time I get a new KDE version.

If we’re talking about online editing, Collabora has web editors based on LibreOffice but with a modern UI: https://www.collaboraonline.com/

They are really great and can be self hosted (e.g. with Nextcloud).

For offline editing, as already mentioned, LibreOffice has an optional ribbon UI and OnlyOffice looks pretty modern as well.

Make sure it has one of the supported chips on that page or it won’t work without extra work.

If not, CC2531 adapters can be bought for very cheap and are perfectly adequate for sniffing Zigbee traffic.

You can still follow that guide if you pick up a cheap Zigbee dongle and connect it to your PC.

You just have to know your network key for decryption and you’re good to go.

Normally, yes, it would say what automation is triggering it, in this case it does not seem to be triggered by an automation.

These are just the reports coming back from the network. So the device reported it turned on/off.

I have these on my individual devices when the group turns on/off.

So the group gets the correct history entry for which automation/user triggered it but all the members of the group just report “Turned on/off”.

Maybe try toggling all your Zigbee groups on and off and see if your misbehaving devices react?

I had this happen once and it was cheap lights that got confused and suddenly started reacting to commands for other addresses. Took me quite a while to figure this out before just throwing them all out.

Starting with the first 2 assumptions, is anyone aware of a means to listening into the ZigBee network to see which device, bridge or middleman, is sending these on/off commands?

zigbee2mqtt has a guide for sniffing Zigbee traffic here: https://www.zigbee2mqtt.io/advanced/zigbee/04_sniff_zigbee_traffic.html

CoreELEC can do it on Dolby Vision certified devices if you’re looking for a open source solution.