Here you can see the same propAMM vs Univ3 5 bps. For small trades the propAMM is tighter, but then widens out for bigger trades This behaviour makes sense while the systems are new and makers are feeling it out. Quoting wide for big trades is a good way to minimise exposure to toxic flow integrate now and ride the wave of tightening spreads on ethereum (link below)

Updates since then: * Deepseek v4 is out. There *is* a 2-bit quant that can run within 90 GB ( huggingface.co/antirez/deeps… ), and it works, however it's only fast on Apple hardware (I've head ~35 tok/s). On AMD, it's ~7 tok/s. IMO actually taking the effort to properly support more than one hardware manufacturer is a great example of the difference between mere "decentralized AI" and genuine "CROPS AI". I hope we can become better at this. * github.com/vbuterin/messagin… also has alpha telegram support now. However, the path to adding your account is quite janky * github.com/Luce-Org/lucebox-… looks promising as a way to run "dense" models (eg. Qwen 27B) more efficiently. It's janky, but on my 5090 laptop it seems to be ~2x more tok/s than llama.cpp * VoxTerm (local AI recording, no third-party servers) continues to be developed github.com/dmarzzz/VoxTerm And there's a lot more projects coming on the horizon. One other thing that has been on my mind is that there's actually a lot of intersection between "CROPS ethereum access layer" and "CROPS AI". For example, we want a ZK way to make (paid) calls to remote LLMs. But if we have this, then it's just as useful for solving another problem: private RPC reads in Ethereum. Another example: application-specific finetuned LLMs. Leanstral ( mistral.ai/news/leanstral ; I get ~38 tok/s on AMD) fits into < 70 GB, but can hold its own against 1T models on writing Lean code. Things like this are a huge boon for writing more secure code ( vitalik.eth.limo/general/202… ). We should have models finetuned for Ethereum-related use cases as well.

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