According to @KyeGomezB, Rust-based Swarms RS runs 100–400x faster than Python agent frameworks, slashing RAM CPU costs for production AI agents.

According to @KyeGomezB, Swarms Rust posts 130×–440× faster startup and 10×–88× less overhead than Python agents, with a new page and demo live.

According to @KyeGomezB, Swarms Rust 0.3.0 is 130×–440× faster with 25×–68× lower memory than LangChain, LangGraph, and CrewAI, boosting agent throughput.

According to @_avichawla, open source CopilotKit brings Claude-style Artifacts, streaming, and persistent threads to any agent via AG UI.

According to @AndrewYNg, a new course titled 'Nvidia's NeMo Agent Toolkit: Making Agents Reliable' taught by @Pr_Brian from @NVIDIA addresses a major challenge in the AI industry: turning agent demos into robust, production-ready systems. The course demonstrates how Nvidia's open-source NeMo Agent Toolkit (NAT) enables teams to enhance agentic workflows, regardless of whether agents are built in raw Python, LangGraph, or CrewAI. NAT offers essential modules for observability, evaluation, and deployment, supporting execution trace visualization, systematic performance evaluations, and CI/CD integration. These features streamline the transition from proof-of-concept to reliable production deployment, opening new business opportunities for AI developers and enterprises striving for scalable and dependable agent-based applications (source: @AndrewYNg, Dec 18, 2025).

According to @DeepLearningAI on Twitter, a new course titled 'Design, Develop, and Deploy Multi-Agent Systems with CrewAI' is now available on Coursera, taught by @joaomdmoura, Co-Founder and CEO of CrewAIInc. This course provides AI professionals with practical training to build collaborative AI agents equipped with advanced features such as tool integration, memory management, and guardrails, enabling scalable handling of real-world workflows. The course addresses the growing demand for AI solutions that automate and optimize complex business processes, highlighting immediate business applications and workflow automation opportunities with multi-agent systems (Source: @DeepLearningAI, Nov 18, 2025).

According to @AndrewYNg, a new course titled 'Design, Develop, and Deploy Multi-Agent Systems with CrewAI' is now available, taught by @joaomdmoura, Co-founder and CEO of CrewAIInc (source: Andrew Ng on Twitter). This course provides hands-on training in building practical AI multi-agent systems that can automate complex workflows by mimicking human team collaboration. Participants will learn to use CrewAI’s open-source framework to create, coordinate, and deploy AI teams, focusing on agent design, task assignment, and system deployment. The curriculum covers building reliable AI agents with tools and guardrails, developing agent teams for planning and coordination, and deploying production-ready AI systems with monitoring and evaluation. This offering addresses growing industry demand for scalable AI workflow automation and equips professionals with in-demand skills for enterprise AI integration (source: deeplearning.ai course page).