💙 Gate Square #Gate Blue Challenge# 💙
Show your limitless creativity with Gate Blue!
📅 Event Period
August 11 – 20, 2025
🎯 How to Participate
1. Post your original creation (image / video / hand-drawn art / digital work, etc.) on Gate Square, incorporating Gate’s brand blue or the Gate logo.
2. Include the hashtag #Gate Blue Challenge# in your post title or content.
3. Add a short blessing or message for Gate in your content (e.g., “Wishing Gate Exchange continued success — may the blue shine forever!”).
4. Submissions must be original and comply with community guidelines. Plagiarism or re
KIP announces the launch of Superior AI Agents, the first AI agent that truly achieves autonomous self-learning
PANews reported on March 13th that Web3 AI underlying protocol KIP Protocol recently released Superior AI Agents, the world's first truly self-learning artificial intelligence framework. Unlike traditional AI that relies on static data and manually set benchmarks, Superior Agents can dynamically evolve, learn, and adapt in real environments without human intervention to achieve preset goals. KIP believes that the only feasible path to Artificial Super Intelligence (ASI) is self-learning AI - capable of independently discovering knowledge, rather than relying on human input. Superior Agents are born under this concept, breaking through the traditional machine learning paradigm, with the ability for self-optimization and independent evolution. Superior Agents have made a significant breakthrough - they are among the first to attempt to autonomously pay for operating costs as AI agents, continuously optimizing their decisions through online trading rather than executing preset strategies. This marks a step for AI from being a passive tool to a self-optimizing, independently evolving intelligent entity. Superior Agents originated from the network security research at the National University of Singapore (NUS). The KIP team had developed an AI prototype capable of autonomous evolution as early as 2020, ultimately breaking through the traditional machine learning paradigm, proving that AI can test its own theories through real-world environments, rather than relying solely on human data memories.