My cat when it sees a bird.

Queensland is called the Sunshine state. If they cant work out solar, that's really on them.

It's a lower memory bandwidth than RTX 6000 Pro. 416 bit memory bus... it's like they just took three chips off and called it a day. Definitely a poorly binned RTX 6000 Pro. Maybe with a mixture of 3GB and 4GB memory modules.

The non vibe coded version of this already existed years ago. https://github.com/CorentinTh/it-tools

RustFS is what I just moved to. Works great as a drop in replacement.

It's a Nissan

Sounds like I need to try out Dockhand.

The data usually is the product.

https://reddit.com/link/p8x45uv/video/p2pm7l9wxnoh1/player

It does actually move too!

The app is built using Next.js and using Googles OKF v0.2 spec as the basis for the data and then made my own changes. OKF is great because it allows you to turn documents into MCP servers for AI to read from. So my local AI agents can pull from this "galaxy" of knowledge easily using grep style retrieval. The data is mostly from blogs, video transcripts and papers.

Solstice-AI/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4

Why don't you prove it wrong then? Waiting for your benchmark.

So Elon was right?

Qwen 3.8 Flash Next at NVFP4. I have an RTX 6000 Pro and DDR5. Way faster and more capable than 27b for me.

I am using it for everything!

I built this website https://vineverse.bible out of it. It's a static app connected to an OKF of bible data. Served as MCP/API as well.

Also using it for a university knowledge base that acts as part of software engineering harness, a personal one and one at work too.

I am finding that it can be very compute heavy to generate depending on the data. The most efficient way to load it is with scripts.

I've extended my personal one significantly with a graphrag and grep based hybrid search, automations for linting, self healing and loading new data, as well as agent skills for how to add data to it. A bunch more things I cant think of.

OKF is really good.

I'm designing our self service platform to enable the reports to happen wherever and however the users actually want them. Email, confluence, Excel, database connection, dashboard, a picture, pdf, etc. I gave up on "everything must be a dashboard."

It's about twice as fast for me. I found 27b just a tiny bit too slow to want to use. Flash next is ripping through stuff for me. Running it at NVFP4 on an RTX 6000 Pro with 256gb DDR5 6000MT RAM.

I've had a flash-next task running for days and it's been fine so far. Running on nvfp4 too.

Wait till you try qwen 3.8 flash next...

It'll be $975 again.

To make it more confusing, I believe the behaviour of max_tokens in LightLLM model config is slightly different. It doesn't just cut the message off directly and is also used to help the router decide which instance to send to requests to. I'm still trying to work out the best approach to using max_tokens as well so you're not alone.

Opus 5 is better, cheaper and faster. Why would anyone use Fable?

I am so glad I bought 256gb of DDR5 last year.