rao-v

You have to understand that Strata and HyperQwen are giving you good looking tokens per second but are not giving you anything close to the bf16 model’s capability (they quantize, worse they quantize KV caches, they approximate expert routing etc.)

The models are so impressive that getting 70-80% of their capability can be helpful, but beware, you are using a significantly dumber model.

+1 I tried quite hard to make things work with tmux, zellij, and even some light custom stuff, but if you have multiple agents running on multiple boxes herdr just works unreasonably well. I really didn't want to like what seemed like a random mix of features, but they've stabilized it to a sensible capability set.

I’m assuming the bitcoin crash is some sort of “if Binance is vulnerable to AI powered (doesn’t have to be true) hacks, then all crypto is less a safe bet and thus worth less?”

Very nice! I'm actually running a autoresearch / modded-gpt comparision to try the complex math operation idea right now! I suspect it's too hard for small quick model training runs to figure out how to use them.

I'll put your knowledge graph nn idea into the mix of things to try.

Ask the least smart LLM you have access to (I tried gemini lite flash) to make a list of ways for processes in microVMs to use a sidechannel attack to talk to each other and you'll get quite a list (including obviously cache hits/misses). Heck a smart CS junior would come up with it. It's well studied!

I’d do this as an RL post training step maybe…

The model already ingests text, so you just to attach a tiny VLM head that reviews the image and read / writes scene continuity text tokens.

Once you have that you can probably just RL with scoring on continuity issues and eventually the VLM head will learn to update dense continuity tokens?

So many fun wierd experiments to run if you have some compute to mess with. There are competent open video diffusion models that you could test this concept easily enough.

I’ve always wondered why we don’t add a few frozen “layers” to models that perform complex computations 2/3rds deep in the model. Some set of steerable fft + some common vector math operations could be reusable units of capability that the model learns to leverage during training.

Given that models learn to use residual connections optimally, it can’t be hard for them to steer around the fixed computation layer when it’s not needed.

I’m sure giant models invest a few layers in reproducing these functions, but surely smaller models can benefit?

I think it’s more that you need really good latency guarantees for 6DOF to not feel terrible - it’ll be pretty hard to get that from a multitasking phone

The Oculus devices have and continue to have terrific support from Facebook

I don’t understand why these world models aren’t designed to be backed by an updatable world state. It could probably just lay out the world onetime and reference it as needed vs trying to invent from wholecloth via diffusion. Object persistence!

Oh I’m aware - my point is it doesn’t even have “sorta” interesting attempts and pads with Claude for paperwork. OpenAI’s equivalent will have a bunch of cool sounding projects (which I agree will struggle to have the promised wins - with frontier general purpose models anyway)

I’m honestly a little surprised by the sparsity of examples? One example of improving an existing therapeutic (Generate Biomedicines), one example of speeding up an existing clever play in food allergies (Divergence Labs), one fairly generic lead optimization play, and then we get … Claude helped write better paperwork and documentation for Enveda?!

I know Anthropic are brilliant at knowing when to focus where, but I genuinely expected them to be talking about 3-5x more serious scaled up drug discovery plays at this stage.

Is this not a focus, or did the big plays not want to be part of a roundup post?

There’s a tiny segment of folks (like me) who have been waiting a very long time for an 8ish inch OLED tablet. For some reason, until mid 2025, they were extremely rare, even in the Android multiverse. I’m excited because I read a ton with white text on black, and an OLED display (especially with this nanotexture stuff) will make a huge difference.