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Master building state-of-the-art voice AI agents with Pipecat's open source tools for natural conversations, customer support, and seamless backend integration.
Discover how VS Code and GitHub Copilot's AI features enhance development through live demos of code generation, chat planning, terminal commands, and custom instructions for real-world scenarios.
Discover the current landscape and future trends of AI startups in 2025 through expert insights from Conviction's Sarah Guo at the AI Engineer World's Fair.
Explore advanced techniques to optimize LLM inference systems, balancing cost, latency, and quality using NVIDIA's Dynamo framework and cutting-edge methods like disaggregation and speculation.
Discover the future of Vision AI technology and its transformative applications across industries in this lightning talk from Roboflow's expert.
Discover practical tactics to transform unreliable AI prototypes into production-ready applications with proven evaluation methods and real-world deployment strategies.
Discover why intuitive "vibe coding" fails in production environments and learn how proper context drives successful software engineering practices beyond just writing code.
Explore practical strategies for building and scaling AI agents in production environments, moving from local implementations to cloud-scale architectures with demos and real-world use cases.
Explore Amazon's approach to building AI agents that can reliably perform computer tasks, featuring Nova Act model and real developer workflows from the AGI SF Lab.
Discover key insights from the 2025 State of AI Engineering report, covering current trends, challenges, and opportunities shaping the AI engineering landscape today.
Explore why the agents vs workflows debate misses the point and discover how to effectively combine both approaches for better AI system architecture.
Discover why endless data center expansion is flawed and explore GPU marketplace solutions for sustainable, democratized AI infrastructure that maximizes existing resources.
Discover how AI agents are evolving from experimental tools to practical coworkers, exploring new infrastructure needs for RL training, test-time scaling, and deployment solutions.
Explore how AI engineering evolves as applications shift from 1:1 user-LLM ratios to 1:100+ intensive workflows, covering standard models and the SPADE framework for AI-heavy apps.
Explore reinforcement learning fundamentals, kernel techniques, LLM quantization to 1.58-bits, and agent creation in this comprehensive AI workshop by Unsloth's founder.
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