Kevin (@xzkevs) — sponsored posts & rates

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Kevin

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@xzkevsCryptoOnchain FinanceAI Agents

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Last 7 days130 posts analyzed · replies not counted · updates weekly

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Kevin@xzkevs · Sep 5one thing i don't like about onchain finance is that everything is so easy to track. you can see where funds came from, where they went and sometimes even figure out someone's strategy just by watching their wallet. that's why confidential transfer feature from @primus_labs is pretty interesting imo using encrypted $EU you can send funds to another wallet on BNB Chain without exposing the transaction details publicly. same onchain experience, just with a little more privacy. because transparency should be a feature, not a requirement to reveal everything.

PrimusPaid partnership515 views

Kevin@xzkevs · Aug 25i gave myself one rule when building this AI stock cart every stock needed a different reason to be here. > NVDA because AI needs the chips to run. > GOOGL because they're building across models, cloud, and AI infrastructure. > MSFT because they're already bringing AI into the products businesses use every day. > META because AI can make its ads, recommendations, and products even better. > AMZN because AWS puts them right in the middle of the AI infrastructure race. 20% each. and since @MEXC 0808 is offering 0 trading fees on eligible stock products, it felt like a good time to build the cart. #MEXC0808

MEXCPaid partnership593 views

Kevin@xzkevs · Aug 25teaching a robot one skill is easy. teaching it to handle the million things that can go wrong? that's the hard part. a robot might learn how to pick up a cup in one environment. but what happens when the cup is somewhere else? - different lighting - different position - different object - different environment suddenly, that "simple" task isn't so simple anymore. that's why I find the data side of Physical AI so interesting. robots need exposure to tons of different situations before they can become truly useful in the real world. and this is where @axisrobotics comes in. their approach is centered around generating diverse robot training data through simulated environments and human interactions. the goal isn't just to teach a robot one way to do something, it's about giving it enough experience to learn how to adapt. I think this is one of the biggest differences between AI that lives on a screen and AI that lives in the physical world. the real world is messy, and robots need to be ready for that. and whoever figures out how to generate high-quality physical AI data at scale could have a massive advantage. we've taught AI to understand words, now we're teaching it to understand the world. and I think we're only at the beginning.

axis roboticsPaid partnership1K views

Kevin@xzkevs · Aug 20everyone is talking about AI agents now, but what happens when AI needs a hand or body? turns out, robots don't learn like language models. they can't just scrape the internet for experience. so who's actually teaching them? that's where things get interesting. i recently came across @axisrobotics and what caught my attention is that they're focusing on one of the biggest problems in Physical AI: - DATA robots need to learn how to move, interact with objects, understand their surroundings and actually complete tasks. and you can't teach all of that with text alone. they need real interactions and demonstrations. that's basically what AXIS is working on, to build infrastructure for generating the training data robots need through simulated tasks and interactions. the more data you can generate, the more these models can learn and improve. i think physical AI is still very early and we're probably underestimating how important the data layer will become. we always talk about better models and better robots but someone still has to teach these robots what to do. we taught AI how to understand information and now we're starting to teach it how to interact with the world. and i think that's going to be a very interesting race to watch. who do you think wins the Physical AI race? the companies building the robots, the models or the data?

axisroboticsPaid partnership738 views

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