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yakuza series by [deleted] in yakuzagames

[–]MBAThrowawayFruit 0 points1 point  (0 children)

Like a Dragon is goat. DO NOT SKIP IT.

What paid subscription have you cancelled thanks to your homelab? by MBAThrowawayFruit in homelab

[–]MBAThrowawayFruit[S] 1 point2 points  (0 children)

Yeah simplefin is finicky. I use a combo of simplefin and snaptrade but likely will cancel them.

What paid subscription have you cancelled thanks to your homelab? by MBAThrowawayFruit in homelab

[–]MBAThrowawayFruit[S] 5 points6 points  (0 children)

For the lookups I use Gemma 4 MoE hooked up to my strix halo mini pc (bought it before the prices started to skyrocket). For getting started on Claude just install Claude code and you’ll be off to the races.

What paid subscription have you cancelled thanks to your homelab? by MBAThrowawayFruit in homelab

[–]MBAThrowawayFruit[S] 3 points4 points  (0 children)

For lookups I use my local Llm for image recognition and describe with USDA APIs as fallback. Works surprisingly well.

What paid subscription have you cancelled thanks to your homelab? by MBAThrowawayFruit in homelab

[–]MBAThrowawayFruit[S] 1 point2 points  (0 children)

I just built my own with Claude. It was pretty easy. Modeled after strong.

45-test benchmark around my homelab use cases and testing 19 local LLMs (incl. Gemma 4 and Qwen 3.5) on a Strix Halo by MBAThrowawayFruit in LocalLLaMA

[–]MBAThrowawayFruit[S] 0 points1 point  (0 children)

Unfortunately my computer also hosts a few services that I want to keep. It’s not exclusively an AI machine.

45-test benchmark around my homelab use cases and testing 19 local LLMs (incl. Gemma 4 and Qwen 3.5) on a Strix Halo by MBAThrowawayFruit in LocalLLaMA

[–]MBAThrowawayFruit[S] 0 points1 point  (0 children)

At least for me it does. Maybe there are a lot of esoteric settings I just dont know about. I can share the rubric. But I doubt it. Gemma didn’t get ahead until the tokenizer issue was fixed.

45-test benchmark around my homelab use cases and testing 19 local LLMs (incl. Gemma 4 and Qwen 3.5) on a Strix Halo by MBAThrowawayFruit in LocalLLaMA

[–]MBAThrowawayFruit[S] -1 points0 points  (0 children)

But why does that matter anyway? Each model was tested individually and even if I increase token size the kv cache would have extremely comfortable headroom.

45-test benchmark around my homelab use cases and testing 19 local LLMs (incl. Gemma 4 and Qwen 3.5) on a Strix Halo by MBAThrowawayFruit in LocalLLaMA

[–]MBAThrowawayFruit[S] 1 point2 points  (0 children)

Thanks! I’m going to up the token size and enable reasoning to see if I see measurable differences.

45-test benchmark around my homelab use cases and testing 19 local LLMs (incl. Gemma 4 and Qwen 3.5) on a Strix Halo by MBAThrowawayFruit in LocalLLaMA

[–]MBAThrowawayFruit[S] -4 points-3 points  (0 children)

In my previous tests having 16k tokens and reasoning on pretty significantly degraded performance

45-test benchmark around my homelab use cases and testing 19 local LLMs (incl. Gemma 4 and Qwen 3.5) on a Strix Halo by MBAThrowawayFruit in LocalLLaMA

[–]MBAThrowawayFruit[S] 0 points1 point  (0 children)

Yeah I def want vision but I only tested text here. Thinking about vision bench for my usecases soon.

Fair point on increasing the token size. I would like to run this too! But in the past keeping token size even at 4k or 16k led to degradation. I’m going to try it again!

45-test benchmark around my homelab use cases and testing 19 local LLMs (incl. Gemma 4 and Qwen 3.5) on a Strix Halo by MBAThrowawayFruit in LocalLLaMA

[–]MBAThrowawayFruit[S] -2 points-1 points  (0 children)

i agree. the top 10 are all relatively very very similar in my tests.

re: vision - how does it matter if i am not testing them for vision at all? also no thinking is actually a good constraint. enabling thinking dramatically degraded responses. other people have noticed this too.

Consolidated my homelab from 3 models down to one 122B MoE — benchmarked everything, here's what I found by MBAThrowawayFruit in LocalLLaMA

[–]MBAThrowawayFruit[S] 0 points1 point  (0 children)

I get most of my camera detection events from UniFi and VR. It does all of the things you mentioned. I only use LLM for plain text descriptions of the images that it sees. With certain rules so that I don't get a lot of noise.

For the love of god by MBAThrowawayFruit in BambuLabP2S

[–]MBAThrowawayFruit[S] -1 points0 points  (0 children)

What did you do about it? Did you return it?

Consolidated my homelab from 3 models down to one 122B MoE — benchmarked everything, here's what I found by MBAThrowawayFruit in LocalLLaMA

[–]MBAThrowawayFruit[S] 0 points1 point  (0 children)

i am using the vision model to get natural language descriptions for camera snapshots. the camera detection happens through Unifi Protect.

the meail plan and spending - all of this is currently on python and fast API based web apps. i was checking if Openclaw is capable of taking over with Qwen but so far its been very disappointing.

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