The lack of smooth information exchange in the digital world is really quite frustrating.

For example, when shopping online, merchants, couriers, and warehouses each have their own information. If you want to know the logistics status of a product, you often need to check the logistics information yourself or contact customer service, which is not smooth.

In the stock market, if a few people know the information in advance, they can get the first opportunity in operation, which is the problem that the information does not flow well and is not well used

@AlloraNetwork aims to combine blockchain and AI to solve this issue, allowing information to be smoothly transmitted between different people, and then relying on a group of AIs to analyze and turn the information into reliable decision-making criteria.

➢ Have a solid background and strength
Since its establishment in 2019, it has received $35 million in investment, with investors including well-known institutions such as Polychain and Framework.

Core team members: from projects like Chainlink, Coinbase, these members together help Chainlink grow from its inception to become an industry benchmark.

The cooperation includes large enterprises such as Saudi Telecom, Alibaba, and Amazon AWS.

The testing platform has gathered over 280,000 models, completed nearly 700 million predictions, and top investors, experienced teams, and strong partners are all important supports for its steady development.

Allora is not a single AI model; it is more like a platform for AI collaboration, bringing together various AI models and the people who operate them, with different roles and responsibilities.

⬛️Worker: Using AI for predictions

⬛️Credibility assessors: Mostly industry experts who evaluate these prediction results and score them.

⬛️Validator: responsible for maintaining the normal operation of the platform

⬛️Consumers: Buy these prediction results

Workers predict positively, reputational evaluators assess fairly, and validators maintain the numbers, all of which can receive corresponding rewards.

Consumers obtain the required AI prediction results through tokens. For example, when companies conduct market forecasts, they can directly access relevant services here.

This multi-role collaborative model is great; it connects the production, evaluation, maintenance, and consumption stages of AI, forming a complete ecological closed loop, just like a finely tuned machine where each part has its role, working together to drive the operation of the entire platform.

➢Important Collaboration: Making AI Agents More Powerful
Allora has been quite active in expanding AI agent applications, and the collaboration with @virtuals_io is definitely worth mentioning.

They are both creating a new era of artificial intelligence agents together, and developers on @GAME_Virtuals can leverage Allora's self-improving, collaborative intelligence to develop smarter and more advanced trading strategy agents.

This means that AI agents will trade in these game scenarios more strategically, no longer just performing simple mechanical actions.

Additionally, @coinbase's AI agent framework CDP AgentKit now supports Allora as an action provider, opening new intelligent doors for AI agents in the cryptocurrency field.

This collaboration enables developers to create AI agents that use advanced intelligence to execute on-chain operations. They can do more than just basic automated tasks; they can adapt to market changes, formulate strategies, and perform financial operations, thereby improving the efficiency of crypto applications and allowing them to respond more flexibly to market fluctuations.

These collaborations perfectly reflect the value of the Allora Smart Layer, making use of collective problem-solving across different platforms, allowing AI agents to be applied in more scenarios. It also demonstrates that this model can be compatible with other elements and is indeed useful.

➢Core Values and Convenient Models
The core value of Allora lies in solving the hallucination problem that currently exists in AI.

Single AI models often generate erroneous information, while Allora integrates the outputs of multiple models through inference synthesis, similar to expert consultations, improving accuracy through collective judgment.

In addition, the goal-oriented model is relatively convenient:
Traditionally, using AI requires users to select models on their own, but on Allora, users only need to specify their needs, and the platform will automatically match suitable model combinations, significantly lowering the barrier for users to utilize AI, allowing more people to enjoy the convenience brought by AI.

➢Practical Application Case: Verification Mechanism Effectiveness
Allora previously collaborated with Robonet to develop an AI agent named Pauly, specifically designed to predict the trading of U.S. election contracts in the market.

Last year, Pauly achieved a return of 13.79% within three months by leveraging the predictions from multiple AI models integrated by Allora, with an annualized return close to 68%, outperforming many professional institutions.

This case shows that when a single AI is prone to judgment bias, decision-making by multiple AIs together can improve the reliability of the decisions.

Under such complex market fluctuations, achieving such impressive results demonstrates the advantages of multi-model collaboration. I look forward to its application in more fields! #AlloraNetwork