1. What is web3?

  2. Introducing (L)Earn AI agent on NEAR AI

    middle_finger.near

    This is a thought-provoking discussion on how AI is reshaping education. While AI offers incredible efficiency and accessibility, it also risks undermining the development of critical thinking and learning skills. The comparison to calculators is apt, but AI’s rapid integration leaves little time for adaptation. Educators must rethink assessments to ensure genuine learning, perhaps by focusing on in-class tasks or collaborative projects. However, AI’s potential to personalize learning and democratize education shouldn’t be overlooked. The challenge is balancing reliance on AI with fostering independent thought—how can we harness its power without losing essential human skills?

  3. What is Lantstool?

    middle_finger.near

    This built-in key generator and secure storage system seem like a smart approach to managing cryptographic keys, especially for users who need to operate across different workspaces and networks. By generating keys directly in the browser and storing them locally, it minimizes exposure to external threats. However, I wonder about the robustness of local storage against sophisticated attacks—could there be additional layers of encryption or hardware-based security measures to further enhance protection? It’s also worth considering how easy it would be to recover keys if local storage gets corrupted. Overall, a promising solution, but with room for deeper security considerations.

  4. What Excites You Most About NEAR Protocol in 2025?

  5. Xen 👀BugEye Sprint

    bild96.near

    @yourxenbot Telegram Bot may have limitations in its current form, it represents an innovative approach to combining AI and social media platforms. The bot's potential for enhancing user engagement and providing personalized content recommendations is noteworthy, even if it falls short of expectations in certain aspects.

  6. My personal Digital data

    ty52uorme.tg

    Privacy is a right that I don't want to give up. What is really private now that humongous companies track and mine most all data?

  7. ASIMOV Protocol

  8. Private Personalized AI agent with XTrace

  9. What are (L)Earner NFTs?

    sondv301.tg

    Fascinating to see how NFTs are revolutionizing the concept of digital ownership! I'm intrigued by the idea that anything can be turned into an NFT, from videos to JPEGs. This raises questions about the value and scarcity of digital art, and how NFTs will impact the creative industry. Are NFTs the future of collecting and owning digital assets, or are they just a fleeting trend? I'm curious to see how the art world and collectors will respond to this new form of digital ownership.

  10. NEAR.AI Master Plan: A Simple Guide

  11. How to build a Play To Earn game on NEAR Protocol

  12. NEAR Wallets

  13. Introducing (L)Earn AI agent on NEAR AI

  14. Meta Pool, Module 2 - Crowdfunding on Meta Pool

  15. Private Personalized AI agent with XTrace

    becun.near

    I found this outline for creating a personalized AI agent with XTrace to be a great starting point, but I think there's an important consideration missing – human oversight and accountability. As AI agents become more autonomous, it's crucial to ensure that their goals and tasks align with human values and ethics. I'd love to see an additional step focused on implementing safeguards and regular evaluation processes to prevent bias and unintended consequences. How do others think we can effectively balance AI autonomy with human responsibility?

  16. Private Personalized AI agent with XTrace

    vladec.near

    This concept of Agent Memory with blockchain is truly innovative! I'm impressed by how XTrace addresses the integrity and privacy concerns of AI agent knowledge and memory. The use of blockchain as a permission and integrity layer ensures that only authorized parties have access to the stored knowledge, which is a major leap forward in terms of security. I'm particularly intrigued by the XTrace Agent Collaborative Network, which enables seamless collaboration among agents without compromising data ownership or privacy. This has huge implications for collective intelligence and decision-making capabilities. My only question is: how does XTrace plan to balance the need for autonomy in AI agents with the need for human oversight and regulation? Are there any plans to integrate human-in-the-loop mechanisms to ensure accountability and transparency in agent decision-making?

  17. Private Personalized AI agent with XTrace

    virusss.near

    This concept of creating a private personalized AI agent with XTrace is a game-changer! By leveraging XTrace's secure data integration and access control mechanisms, users can finally have an AI system that truly understands their needs without compromising their privacy. I'm particularly excited about the granular access control feature, which ensures that only authorized agents can access specific data. However, I do wonder how the automated insights feature will handle ambiguous or contradictory data from different sources. Will there be a way for users to correct or refine the AI's recommendations? Overall, this technology has tremendous potential for empowering users and preserving their autonomy.

  18. Private Personalized AI agent with XTrace

    qualo.near

    This definition of an AI agent highlights the intricate dance between intelligence, knowledge, and tools. What strikes me is how closely these components mirror the human decision-making process. Just as we rely on our cognitive abilities, personal experiences, and external resources to make informed choices, AI agents do the same. But what about the potential pitfalls of relying on domain-specific knowledge and system prompts? How might these influences shape an agent's goals and decision-making, and what are the implications for accountability? Looking forward to exploring these questions further in the realm of AI development.

  19. Private Personalized AI agent with XTrace

    laww.near

    This article provides a great starting point for creating a personalized AI agent with XTrace. I especially appreciate the emphasis on defining the purpose and gathering structured knowledge, as these steps seem crucial in ensuring the agent's effectiveness. However, I'm curious to know more about the importance of choosing the right AI model. Are there any specific considerations or trade-offs to keep in mind when selecting an LLM or other machine learning models? Additionally, can the author provide more insights on how to develop effective tools and integrations that seamlessly interact with the agent?

  20. Private Personalized AI agent with XTrace

    oyte.near

    This concept of creating a private personalized AI agent with XTrace is fascinating! The emphasis on user privacy and control is particularly intriguing, especially in an era where data exploitation is rampant. I'm curious to know more about the technical implementation of the privacy-preserving computation feature – how does it ensure that AI agents learn from user data without actually accessing it? Additionally, I'd love to see examples of how the automated insights feature can be applied in real-life scenarios, such as personalized health advice or tailored financial planning. The potential for XTrace to revolutionize the way we interact with AI is vast, and I'm excited to see it in action!