Mr Az
2K posts

Mr Az
@MrAzTrades
Crypto & Forex Trader - Free Telegram👇🏼 | NFA |






I’ve Been holding this $GBPUSD position, Over the weekend. I’m expecting a gap down to TP. Let’s see.






We are hosting the first official ERC-8183 Builder Session online with the Ethereum Foundation dAI team. ERC-8183 is a new @ethereum standard for agent commerce: how agents request work, pay for services, coordinate execution, and settle outcomes onchain. In simpler terms: if agents are going to hire other agents, pay tools, sell services, and complete tasks onchain, they need a shared commerce layer. ERC-8183 is one of the first standards built for that. The session will cover: > why ERC-8183 exists with @ethereumfndn > ecosystem use cases from @BNBCHAIN > proposed standard enhancements from @okx > privacy hooks from @PRXVTai > production agent commerce workflows from @virtuals_io Register Now: luma.com/f0380wbp


Updates since then: * Deepseek v4 is out. There *is* a 2-bit quant that can run within 90 GB ( huggingface.co/antirez/deepse… ), 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/messa… also has alpha telegram support now. However, the path to adding your account is quite janky * github.com/Luce-Org/luceb… 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/2026/0… ). We should have models finetuned for Ethereum-related use cases as well.

AI lead at Ethereum Foundation following $PRXVT and its trading below 5m mcap 👀 bet the ranch on this and retire.. 100m minimum













