Tom Smith makes an interesting case comparing the current agent landscape to the pre-protocol web era and that analogy sticks. Curious how quickly real teams will actually adopt an AGNTCY layer instead of continuing to build custom bridges out of habit.
Linux Foundation's new AGNTCY project promises to solve AI agent fragmentation with open standards.
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LLM Training & Evaluation Specialist with hands-on experience building major AI models. As one of th... Show moreLLM Training & Evaluation Specialist with hands-on experience building major AI models. As one of the original six members of Google's Bard training team (now Gemini) and current Meta AI Business Assistant evaluator, I understand how these models work from the inside out—and how developers can optimize them for production applications.
I specialize in LLM evaluation, prompt engineering, and RLHF (Reinforcement Learning from Human Feedback) methodologies. My focus is helping developers integrate LLMs into production systems: model fine-tuning strategies, prompt optimization, agentic workflows, AI-powered DevOps, and building reliable AI applications that actually work.
Having trained the core Google Bard model and interviewed 4,000+ technology executives across AI/ML infrastructure, I write about real-world LLM implementation challenges—not theoretical possibilities. I attend major tech conferences to understand what developers actually face when deploying AI in production environments. Show less
I specialize in LLM evaluation, prompt engineering, and RLHF (Reinforcement Learning from Human Feedback) methodologies. My focus is helping developers integrate LLMs into production systems: model fine-tuning strategies, prompt optimization, agentic workflows, AI-powered DevOps, and building reliable AI applications that actually work.
Having trained the core Google Bard model and interviewed 4,000+ technology executives across AI/ML infrastructure, I write about real-world LLM implementation challenges—not theoretical possibilities. I attend major tech conferences to understand what developers actually face when deploying AI in production environments. Show less
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