Let Mira take a look, your AI is' real ' ✅” Still 'fake' ❌” Recently, there have been signs of recovery in various AI sectors. Compared to my previous hasty understanding of some AI projects, I am planning to delve deeper into experiencing and researching AI projects that I like or find interesting. Today, I would like to share @ MiraN_Network. Although its popularity has been good recently, users may not fully understand what it does, especially since it doesn't have any coins and its white paper is not very easy to read. one ️⃣ What does Mira do? Simply put, Mira helps you verify whether what AI gives you is real or fake. For example, if you ask AI what the weather is like today, it's clearly raining ☔️， But AI tells you through various data analysis and reasoning that today is sunny ☀️。 When you raise doubts, AI will still prove itself and not repent. This is just one example, there are many other scenarios, and many friends who use AI tools may have encountered this problem. That is, what AI gives you is fake, but you are not aware of it, commonly known as "AI illusion" Mira was born to solve this problem, which is why it is positioned as an important infrastructure for AI. two ️⃣ How does Mira achieve her vision? In the current information, I think Messari is pretty good. Link: https://(Messari. io)/report/understanding ai-verification a-use-case-for-mira? utm_source=twitter&utm_medium=organic_social&utm_campaign=pulse&destination=protocol_services_research Because what AI gives you is the result of multiple sources of information, big models, etc. Recently, Grok's question about South Africa also highlighted the bad experience brought by human intervention in big models. Therefore, for Mira, his protocol design comes from several important criteria: -The accuracy of facts should not depend on the output of a model. -Verification must be autonomous and not rely on constant human supervision. -Reliability should be derived from independent protocols rather than centralized control. How to do it specifically, I understand that the core is "modularizing AI results for distributed verification", as shown in the following figure. For example, now there is an AI that provides content. In Mira, its processing flow is as follows: AI content ->Disassemble into different modules ->Verify by different nodes ->Obtain reliability results for different modules ->Ultimately define the authenticity of AI. At present, the operational nodes include ionet, aether, hyperbolic, exabits, and spheron, and the report states that they process over 1 billion tokens per day. three ️⃣ How do you play? Of course, Mira is not just in the white paper stage now. They have already collaborated with many AI projects and launched their own products. For example, the agent shared by @ karansirdesai, you can verify the authenticity of the content and generate a report. The following picture shows some experience links 🔍 https://(x.com)/karansirdesai/status/1916137719572664606 In addition, there is currently a Voice of Realm activity where users can participate in a 15k incentive program by using Mira's official AI assistant, Mira Scrolls, and training based on project documents/announcements for assisted creation 💰 https://www. (techflowpost.com)/newsletter/detail_83825.html Another way to earn points is to experience the @ klok'app software 🤖 The link is as follows https://klokapp.ai?referral_code=67CKRM8S Official website: https://mira.network/ More information: https://mira.network/research/mira-whitepaper.pdf