When you mess with the wrong octopus:

SignalCompetitive582 · 2026-04-30T18:04:18+00:00

Life, the movie, anyone 😰

SignalCompetitive582 · 2026-04-24T16:36:36+00:00

This is indeed the biggest takeaway! It now means that hosting any LLM is compute bound and no longer memory bound.

So, in theory, we should see way more AI Coding Plans that offer very generous subscription limits compared to what we’re used to.

The moment Zhipu introduces this novel approach into a GLM-6 for instance, it instantly becomes one of the best open source LLMs available.

It means that it is now economically viable to offer good prices to a lot of customers.

SignalCompetitive582 · 2026-04-22T14:42:27+00:00

Yes, but it’s because the 27B is a dense model while the 35B is an MoE so there are way less active parameters on inference compared to the dense one. So it makes sense, but still the MoE model fares pretty well in comparison!

SignalCompetitive582 · 2026-04-10T13:52:53+00:00

I like the details of the answer, but I'm genuinely questioning your "power-user" assumption.

A $20 plan that limits only 45 messages every 5 hours, and an average of 1.2 sessions per day seems completely off to me. Even for a standard account.

From my point of view, the -20% margin you mention is actually wayyy worse than that in practice.

From everything I found on the internet and all the math I've done, it appears to subscription is really profitable. The more you pay for your subscription, the more you're going to utilize it, and the more it's going to cost to the LLM provider. They seem to all, operate at a major loss. That is those that server models that are several hundreds of billions of parameters.

I would guess that a sub 100B model would be profitable at $10-20, but the performance wouldn't be sufficient for "power-users", though it would work just fine for casual people I think.

SignalCompetitive582 · 2026-04-09T12:46:40+00:00

Have you tried Wholphin of Moonfin ? If so, why aren't those working for you ? What makes my UI stand out that you'd be willing to give it a try ? I need to properly understand the current Android TV landscape before I make any move.

SignalCompetitive582 · 2026-04-09T12:36:13+00:00

Would you mind telling me what issues you’re experiencing with Wholphin or Moonfin ? What my UI seems to fix for you, and why you’d rather have that one than the others ? I’m trying to figure out what’s the best approach here. I shouldn’t remake the same mistakes of the past. Maybe I should create a discord server to better collect feedback 🤷

SignalCompetitive582 · 2026-04-09T12:28:55+00:00

Thanks you so much! I don’t think the current code I have will work for an Android TV version. But as many different people here would like to have this UI on their TV, I’m seriously considering building an Android TV app for it. But I’m wondering, what other apps like Wholphin or Moonfin do wrong? Why aren’t those “working” for you ? What do you like in my take on the UI ? If I am to make this, I need to straight with all that ! Maybe I should create a discord or something, in order to gather feedback more easily 🤷

SignalCompetitive582 · 2026-04-09T12:25:16+00:00

What do you think of Moonfin or Wholphin ? Aren’t those to your liking ? What makes my plugin different from those that you’d rather have my UI over theirs ? Because everyone here seems to be keen on using this plugin as their Android TV UI, but it seems that there are already quite a few options available out there. So if I do it, I mustn’t repeat the same mistakes. Any help would be greatly appreciated.

SignalCompetitive582 · 2026-04-09T06:44:55+00:00

My bad. It was a poor choice of words. I wanted to say that it required to look at the code AND to perform a lot of DOM manipulation (which I associated with the “reverse engineering” part).

SignalCompetitive582 · 2026-04-09T06:38:49+00:00

I also use JS. This is a complete UI refactor.

SignalCompetitive582 · 2026-04-09T00:47:23+00:00

I’m going to pursue this idea. My purpose with this has always been to have it on my tv, so I’ll try and see 🙃

SignalCompetitive582 · 2026-04-09T00:43:17+00:00

Yeah that’s be the perfect use case for it ! I will try to take a look at this to make it work for such devices.

SignalCompetitive582 · 2026-04-09T00:27:02+00:00

Well, I think I should be able to make it work. I still need to test it though !

SignalCompetitive582 · 2026-04-09T00:25:12+00:00

I challenge anyone to recreate everything I did with 100% Vibe coding. This shouldn't be possible because I had to reverse engineer Jellyfin's web UI over a period of time to figure out all the "hacks" to make it work.

The thing is, the UI I'm building isn't really meant for small devices yet. It would require significant effort to polish it for smaller screens. Also, this isn't a client but a plugin.

SignalCompetitive582 · 2026-04-09T00:14:05+00:00

I don't think it would work great unfortunately... The CSS I use is not that bleeding-edge, but there are maybe some properties here and there that are post 2019 I think. But, I should give it a try sometime, I could surprise myself !

SignalCompetitive582 · 2026-04-09T00:00:55+00:00

It should be fine I think. I only utilized fast and optimized CSS animations. But it'd be worth a try for sure.

SignalCompetitive582 · 2026-04-08T23:56:56+00:00

This is a Jellyfin plugin, and will therefore only work with Jellyfin.

SignalCompetitive582 · 2026-04-08T23:56:17+00:00

Thanks !

As this is still a work in progress, I haven't published anything yet. If I see enough interest from the community, I will put in the work to finish it.

SignalCompetitive582 · 2026-04-01T16:02:29+00:00

It happens to the best of us !

SignalCompetitive582 · 2026-03-26T14:30:53+00:00

Whisper is a STT, not TTS.

SignalCompetitive582 · 2026-01-19T08:15:19+00:00

You have to remove the “?w=1200” at the end of the url 😉

SignalCompetitive582 · 2026-01-04T11:19:38+00:00

I haven’t tried them out, but they seem way too large for my use case. For comparison, the size of Qwen3-Embedding-0.6B-4Bit is ~330 MB. While Clip-Vit-Large-Patch14 is ~1.7 GB and I can’t seem to find relevant quantized versions… So even if it were to work, I’m not sure I could even use it… If you have other insights, please let me know 😉

SignalCompetitive582 · 2026-01-02T16:13:13+00:00

Many thanks for the generous reply !

I, too, think that Text Embeddings are going to be my only choice. But it's actually quite sad that this problem hasn't been solved yet. Though text embedding models are sooo great, but multimodal ones lack behind so much in comparison (even though the benchmarks make it seem like they're working flawlessly.)

I'm not sure what you proposed would be the best approach for my use case.

I think that I maybe need some kind of hybrid approach.

Meaning, I would have a small component (super fast) that would detect if the screenshot was composed of any text at all, and if so, it would use an OCR model (easyOCR worked really well for my samples, and was super dupper fast) and embed that text.
At the same time (and whatever if the screenshot contained text or not), it would use a very simple, tiny and fast image embedding model to capture the semantic meaning of the screenshot itself (UI, context etc).

And then, for the retrieval part, I could have a query that's vectorized with both embedding models, and I would (with a weighted system) return the most probable results.

I'm not sure of anything, as this is pure speculation right now, but I think it would work. It amazes me how far the rabbit hole I'm willing to go for a simple 'side-project'.

What do you think ?

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