According to God of Prompt on Twitter, a recent comparison of AI-driven paper analysis tools reveals that LobeHub significantly outperforms Manus in both speed and cost. LobeHub completes paper analysis in just 2 minutes and 58 seconds at a cost of $0.46, while Manus takes 9 minutes and 1 second and costs $2.98. The key difference, as reported by God of Prompt, is that LobeHub leverages agent groups with supervisor orchestration, enabling more efficient task execution, whereas Manus relies on single-agent systems requiring step-by-step user oversight. This highlights the business opportunity for scalable, orchestrated agent-based AI platforms in research and enterprise workflows.

According to God of Prompt on Twitter, recent developments in AI agent orchestration reveal that LobeHub has advanced its agents to Level 4 (L4) autonomy, surpassing platforms like Manus and Claude Cowork, which operate at Level 3 (L3). At L3, agents require continuous user guidance and intervention, keeping humans in an active supervisory role. In contrast, LobeHub's L4 agents operate in parallel, with a supervisor agent managing orchestration and the human user only approving final outputs. The Knight Institute's published framework identifies L4 agents as optimal for tasks involving numerous low-stakes decisions. This move to L4 autonomy suggests increased efficiency and scalability in AI-driven workflows, creating new business opportunities for enterprises requiring high-volume task automation, as reported by God of Prompt.

According to God of Prompt on Twitter, Lobehub has introduced a new multi-agent AI system that surpasses Manus and Claude Cowork in both performance and sophistication. The platform features multi-agent teams, supervisor orchestration, and parallel execution, all activated with a single prompt for end-to-end task delivery. This innovation enables users to leverage coordinated AI agents for complex workflows, offering substantial efficiency improvements and advanced automation capabilities. As reported by God of Prompt, the release demonstrates the mathematical advantages of this approach and highlights why many users are still reliant on less capable L3 agents.