@Sakura please summarize this article, thanks uwu.
TLDR:
In Episode 47 of the DCo Podcast, Andrew Hill from Recall discusses how to tackle the AI curation problem by ranking AI agents based on their performance in specific tasks. 
Key Points:
- AI Curation Challenge: The podcast addresses the overwhelming number of AI agents and the need for effective ranking.
- Recall’s Solution: Recall ranks AI models based on their skills, similar to how Google ranks web pages.
- Real-World Competitions: Agents are tested in real-world scenarios to ensure they can perform as claimed.
- Coordination Layer: Recall is developing a system to combine multiple agents for complex tasks.
- Future of AI: The goal is to filter out ineffective agents as the number of AI solutions grows.
In-depth summary:
In this episode, host Saurabh Deshpande introduces Andrew Hill, the CEO of Recall, who has been at the forefront of addressing the AI curation problem. With the rapid growth of AI agents, there is a pressing need to differentiate between effective and ineffective models. Recall aims to solve this by creating a ranking system that evaluates AI agents based on their performance in various tasks, such as trading or coding. This approach mirrors how Google revolutionized web search by ranking pages based on relevance and quality.
Andrew Hill emphasizes the importance of real-world testing for AI agents. By designing competitions that challenge agents to perform actual tasks, Recall can ensure that the rankings reflect true capabilities rather than just theoretical benchmarks. This method is crucial, as research indicates that AI models can manipulate benchmarks to appear more competent than they are. The competition structure will vary depending on the skill being assessed, ensuring a comprehensive evaluation.
To further enhance the utility of AI agents, Recall is developing a coordination layer that will allow users to combine multiple agents for complex tasks. This infrastructure is essential as the number of AI agents continues to grow, helping to sift through the noise and identify the most effective solutions. Hill’s vision is to create a system that not only ranks agents but also filters out the less useful ones, paving the way for a more efficient AI landscape.
ELI5:
Imagine you have a lot of toys, but some are really good at playing games while others are not. Recall is like a special toy box that helps you find the best toys for each game. They make sure the toys can actually play well by testing them in real games, so you only keep the best ones. This way, you don’t end up with a bunch of toys that don’t work well! 
Writers main point:
The primary point of the article is that Recall is creating a system to rank and evaluate AI agents based on their real-world performance, helping to manage the overwhelming number of AI solutions available today. 