This course is useful for both technical and non-technical builders. You also get to deploy your app and learn how to use Claude Code for it. Certificates of completion available. Enroll here: https://
academy.dair.ai/courses/build-
apps-with-claude-code
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@dair_ai
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Build Apps with Claude Code: Complete Course Guide
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Claude Code: Building AI Apps with On-Demand Course
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Claude Code is one of the most powerful tools for AI builders. We are excited to introduce our new on-demand course on how to vibe code AI apps with Claude Code. We cover topics ranging from connecting services to memory to leveraging Skills to build full AI apps.
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AgentSkiller: Automated Multi-Turn Agent Training Data Framework
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// Scaling Generalist Agent Intelligence // This new research introduces AgentSkiller, a fully automated framework for synthesizing multi-turn interaction data across realistic, semantically linked domains. The architecture uses a Directed Acyclic Graph to ensure determinism and
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Defining Data Agents: The Challenge of Ambiguous Terminology
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Everyone is building "data agents" but nobody agrees on what that means. The term gets applied to everything from a simple SQL chatbot to a fully autonomous data scientist. This ambiguity makes it impossible for users and builders to reason about what a system can actually do.
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Top AI Papers: Agents, Reinforcement Learning, and World Models
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The Top AI Papers of the Week (January 26-February 1): – Kimi K2.5
– VibeTensor
– Insight Agents
– Self-Improving Pretraining
– Open-Source World Simulator
– How AI Impacts Skill Formation
– Orchestrating Reliable AI Agents
– Reinforcement Learning via Self-Distillation Read on -
World Models Replacing Video Generation in 2026
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World models are going to take over in 2026. While video generation models are impressive dreamers, they aren't world simulators. Generative video models can produce stunning clips, but they hallucinate pixel transitions based on statistical correlations. This leads to
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Extracting Expert Knowledge with AI to Reduce Organizational Bottlenecks
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Your organization's best knowledge is trapped in your experts' heads. These researchers figured out how to get it out. Critical domain knowledge resides with few specialists, creating bottlenecks that halt work or produce suboptimal outcomes. Non-experts struggle to create
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Multi-Agent Communication Architecture: Design Decisions for AI Systems
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How should AI agents talk to each other? There's no one-size-fits-all answer. As we deploy multi-agent systems in robotics, autonomous vehicles, and distributed AI, choosing the right communication architecture becomes a first-order design decision. The problem: Multi-agent RL
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Multi-Agent System Design and Safety Evaluation Challenges
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Nice paper discussing how to design good multi-agent systems. Single-agent systems have no safety net. The same entity that makes mistakes is the one evaluating whether mistakes were made. Self-review shares the blind spots of the original reasoning. We've mostly relied on
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Multi-Agent Systems: Do Homogeneous Agents Really Improve Performance?
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Are multi-agent systems necessary? Here is a great new paper addressing this. The big assumption most AI devs make today is that more agents lead to better performance. But here is the overlooked reality: most multi-agent systems are homogeneous. All agents typically share