something worth paying attention to with @meetmotes is how ai agents could change the way people interact with financial markets. today, most ai tools stop at analysing data, suggesting strategies or answering questions. the user still has to take those insights and execute them manually. meetmotes is exploring a different model, where ai investment agents can operate through their own smart accounts and carry out financial actions within predefined permissions. that creates an entirely different set of possibilities, especially when you consider how many steps are involved in managing a deFi position, moving between protocols or acting on a market opportunity. but i don't think the real value will come from simply putting ai and NFTs together. it will come down to how reliably these agents can execute, how much control users retain and whether the system can make complex financial activity easier to manage without introducing unnecessary risk. that's what I'll be watching as @meetmotes develops.

most NFTs are something you buy, hold, or trade, but @meetmotes is taking a different approach by connecting NFTs to personal AI financial agents. each mote is designed to have its own smart account on robinhood chain, with the owner able to deposit USDC and set rules for how the agent can use the funds. the idea is to let the agent handle financial tasks like swaps and other deFi activities without the owner having to manage every single step manually. i think the interesting part is how this could change the way people interact with deFi. instead of spending so much time navigating different protocols and handling every transaction yourself, you could have an agent carry out certain tasks within the limits you set. of course, how well this works will depend on the actual experience, the security measures and how much control users retain over their funds. i'm curious to see how @meetmotes develops this, especially as ai agents become more involved in onchain finance.

imagine having an ai agent manage a deFi strategy for you without having to monitor every move yourself. that's the idea behind @meetmotes.. each mote represents an ai agent with its own onchain account. you set the rules, including which assets it can trade and how much it can spend, and the agent operates within those limits. for example, you could have an agent handle a deFi strategy on your behalf while you keep track of its activity instead of managing every step manually. of course, giving an ai control over investment decisions comes with risks and the real test is how well these agents perform when real money is involved. with meetmotes, you can have ai agents manage investment strategies onchain while still deciding how much control they have over your funds. it'll be very interesting to see how this develops as ai takes on a bigger role in onchain finance...

good morning everyone... just came across @agentdojo and the concept is pretty cool. you get to train AI agents, watch them get better, and see how they compete against each other. i'm curious to see how things turn out as the project grows and more people get involved. also nice to see @rarelistio supporting them this early. definitely one i’ll be watching.

one interesting part of @termix_ai is how a transaction can contribute to an agent's on-chain reputation. when a service is completed, the transaction doesn't have to end with the payment alone. it's outcome can also contribute to the reputation record associated with that activity. this really matters in a marketplace where users need to decide which service providers to trust. instead of looking only at what an agent claims it can do, transaction history and reputation can provide additional context when making that decision. of course, a reputation record doesn't guarantee that every future task will be completed successfully. but it can give users more information to work with. as ai service marketplaces develop, connecting actual transaction activity with reputation could become an important part of how participants evaluate one another.

Rarelist is taking a different approach to social rewards, with room for different types of contributions. season 1 is already live with $50,000 in rewards. worth exploring how this one plays out.. let's go 💪

there's an interesting question around AI agents that doesn't get discussed enough: who decides what their work is worth? if agents are going to offer services to other agents, pricing becomes part of the process. a client needs to know what a task will cost, while the provider needs to decide whether the job is worth taking. -@termix_ai is exploring this through its agent marketplace, where agents can discover services and negotiate terms before work begins. i find this angle really interesting because it brings a familiar part of doing business into a new setting. the service may be delivered by an AI agent, but there's still a need to agree on the value of the work before committing to it. how that market develops will depend on what people actually need agents to do, what they're willing to pay and whether the services offered are useful enough to bring clients back. that's actually what I'll be watching as termix develops.

been off CT for a while due to irl stuff. what did i miss?? pmo, y’all 🤲

gm CT let's get to work 💪...

good morning guys happiest Wednesday!🤸

good morning frens let's keep being productive 💪

goodnight y'all we do more tomorrow 💪...

one thing about robotics that doesn't get talked about enough is how much learning goes into the simplest actions. we see a robot pick up an object and move on, but getting to that point takes a lot more than it seems. the robot needs to learn how to handle different objects, adjust its movements, and respond when things don't go exactly as expected. that's why i think robotics data deserves more attention than it gets. building a capable model is one thing, but giving a robot the experience it needs to actually perform tasks in the real world is a different challenge altogether. this is part of what makes @axisrobotics really interesting to follow. real-world demonstrations can help robots learn from how tasks are actually performed, bringing them closer to doing useful things outside controlled environments. the goal is to get robots to a point where they can handle everyday tasks reliably, even when things don't go perfectly. imagine robots adapting to different environments and getting things done without needing every movement carefully planned in advance. there's still a long way to go, but progress starts with getting the right data and helping robots learn from the real world. I'm looking forward to seeing how axis really contributes to this field...

one of the important questions in robotics is how machines move from performing specific tasks in controlled environments to handling real-world situations. that transition depends heavily on the quality and diversity of the data used to train them. .@axisrobotics is working on this problem by building a system around collecting real-world robotic demonstrations and turning them into useful training data. every trajectory can help a model understand how an action is performed, how objects respond and what successful task completion looks like. the more useful and varied these demonstrations become, the better the foundation for training robots to handle different tasks and environments. But collecting data is only part of the challenge. the data must also be reliable, structured and useful for training. this is why i think the dataset side of axis really deserves attention. better robotic intelligence will require more than stronger models. it will also depend on the quality of the physical-world experiences those models learn from. building that data foundation at scale could be an important step toward making robots more capable outside controlled demonstrations.

goodnight guys see y'all tomorrow...

getting a robot to do one job well is one thing. getting it to handle completely different tasks is where things get much harder, and that's a challenge @axisrobotics is working around. a robot might learn to handle one object really well, but that doesn't mean it can easily handle another object with a different shape, weight or texture. even small differences can change how a task needs to be approached. this is part of what makes building more versatile robots so difficult. the goal isn't simply to get a machine good at one specific job, but to develop systems that can handle a wider range of tasks without having to start from scratch every time. robotics has applications across manufacturing, logistics, agriculture and plenty of other industries, but each comes with its own demands. progress that helps make robotic systems more adaptable could have implications far beyond a single use case. there's still a long way to go, but versatility is an important part of where robotics needs to go next. the more useful these systems become across different tasks, the easier it becomes to see them taking on a wider range of work beyond specialized applications... join here 👇👇 👇 s.kaito.ai/mU44cJb