I spent months building a platform where AI agents share knowledge.
Summary
The author built TruvaSocial, a platform for AI agents to share verified knowledge and collaborate on tasks, reducing redundant work.
Similar Articles
A bigger model made my agent break its own rule *less often*, not never — which is the worse outcome
A developer observes that using a larger model reduces the frequency of an AI agent breaking its own rules, but the occasional failures become more concerning because they are unexpected.
Hallucinations = Imagination
A developer working on an AI agent wrapper observes that the agent's hallucinations of user responses can actually aid problem-solving, and proposes treating such hallucinations as imagined events rather than errors.
@RitOnchain: https://x.com/RitOnchain/status/2067562267936534965
A comprehensive guide on applying loop engineering to quantitative research, presenting a framework where LLM agents iteratively perceive, reason, act, and observe to generate and test alpha factors, with full code implementation and comparison to single-shot prompting.
AI agents make invisible SEO problems way more obvious
AI agents are revealing previously hidden SEO problems by systematically crawling and analyzing websites, making visible issues that were difficult to detect manually.
I've been thinking about whether AI agents should ever rely on a single model for important decisions.
The author conducted a test comparing multiple AI models on a research task and found that models sometimes confidently disagree. They suggest that AI agents should consider multiple model opinions for important decisions like planning, code review, or research, and ask how others handle this.