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		<title>Generative AI in the Real World</title>
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		<description>In 2023, ChatGPT put AI on everyone’s agenda. In 2024, the challenge will be turning those agendas into reality. In Generative AI in the Real World, Ben Lorica interviews leaders who are building with AI. Learn from their experience to help put AI to work in your enterprise.</description>
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		<itunes:subtitle>Now, next, and beyond: Tracking need-to-know trends at the intersection of business and technology</itunes:subtitle>
		<itunes:author>Radar</itunes:author>
		<itunes:summary>In 2023, ChatGPT put AI on everyone’s agenda. In 2024, the challenge will be turning those agendas into reality. In Generative AI in the Real World, Ben Lorica interviews leaders who are building with AI. Learn from their experience to help put AI to work in your enterprise.</itunes:summary>
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<item>
	<title>Generative AI in the Real World: The Year in AI with Ksenia Se</title>
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	<pubDate>Thu, 11 Dec 2025 12:42:43 +0000</pubDate>
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	<description><![CDATA[As the founder, editor, and lead writer of Turing Post, Ksenia Se spends her days peering into the emerging future of artificial intelligence. She joined Ben to discuss the current state of adoption: what people are actually doing right now, the big topics that got the most traction this year, and the trends to look [&#8230;]]]></description>
	<itunes:subtitle><![CDATA[As the founder, editor, and lead writer of Turing Post, Ksenia Se spends her days peering into the emerging future of artificial intelligence. She joined Ben to discuss the current state of adoption: what people are actually doing right now, the big topi]]></itunes:subtitle>
	<content:encoded><![CDATA[As the founder, editor, and lead writer of Turing Post, Ksenia Se spends her days peering into the emerging future of artificial intelligence. She joined Ben to discuss the current state of adoption: what people are actually doing right now, the big topics that got the most traction this year, and the trends to look [&#8230;]]]></content:encoded>
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	<itunes:summary><![CDATA[As the founder, editor, and lead writer of Turing Post, Ksenia Se spends her days peering into the emerging future of artificial intelligence. She joined Ben to discuss the current state of adoption: what people are actually doing right now, the big topics that got the most traction this year, and the trends to look [&#8230;]]]></itunes:summary>
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<item>
	<title>Generative AI in the Real World: The LLMOps Shift with Abi Aryan</title>
	<link>https://www.oreilly.com/radar/podcast/generative-ai-in-the-real-world-the-llmops-shift-with-abi-aryan/</link>
	<pubDate>Thu, 20 Nov 2025 12:16:32 +0000</pubDate>
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	<description><![CDATA[MLOps is dead. Well, not really, but for many the job is evolving into LLMOps. In this episode, Abide AI founder and LLMOps author Abi Aryan joins Ben to discuss what LLMOps is and why it’s needed, particularly for agentic AI systems. Listen in to hear why LLMOps requires a new way of thinking about [&#8230;]]]></description>
	<itunes:subtitle><![CDATA[MLOps is dead. Well, not really, but for many the job is evolving into LLMOps. In this episode, Abide AI founder and LLMOps author Abi Aryan joins Ben to discuss what LLMOps is and why it’s needed, particularly for agentic AI systems. Listen in to hear w]]></itunes:subtitle>
	<content:encoded><![CDATA[MLOps is dead. Well, not really, but for many the job is evolving into LLMOps. In this episode, Abide AI founder and LLMOps author Abi Aryan joins Ben to discuss what LLMOps is and why it’s needed, particularly for agentic AI systems. Listen in to hear why LLMOps requires a new way of thinking about [&#8230;]]]></content:encoded>
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	<itunes:summary><![CDATA[MLOps is dead. Well, not really, but for many the job is evolving into LLMOps. In this episode, Abide AI founder and LLMOps author Abi Aryan joins Ben to discuss what LLMOps is and why it’s needed, particularly for agentic AI systems. Listen in to hear why LLMOps requires a new way of thinking about [&#8230;]]]></itunes:summary>
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	<googleplay:explicit>No</googleplay:explicit>
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<item>
	<title>Generative AI in the Real World: Laurence Moroney on AI at the Edge</title>
	<link>https://www.oreilly.com/radar/podcast/generative-ai-in-the-real-world-laurence-moroney-on-ai-at-the-edge/</link>
	<pubDate>Thu, 13 Nov 2025 13:59:19 +0000</pubDate>
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	<description><![CDATA[In this episode, Laurence Moroney, director of AI at Arm, joins Ben Lorica to chat about the state of deep learning frameworks—and why you may be better off thinking a step higher, on the solution level. Listen in for Laurence’s thoughts about posttraining; the evolution of on-device AI (and how tools like ExecuTorch and LiteRT [&#8230;]]]></description>
	<itunes:subtitle><![CDATA[In this episode, Laurence Moroney, director of AI at Arm, joins Ben Lorica to chat about the state of deep learning frameworks—and why you may be better off thinking a step higher, on the solution level. Listen in for Laurence’s thoughts about posttraini]]></itunes:subtitle>
	<content:encoded><![CDATA[In this episode, Laurence Moroney, director of AI at Arm, joins Ben Lorica to chat about the state of deep learning frameworks—and why you may be better off thinking a step higher, on the solution level. Listen in for Laurence’s thoughts about posttraining; the evolution of on-device AI (and how tools like ExecuTorch and LiteRT [&#8230;]]]></content:encoded>
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	<itunes:summary><![CDATA[In this episode, Laurence Moroney, director of AI at Arm, joins Ben Lorica to chat about the state of deep learning frameworks—and why you may be better off thinking a step higher, on the solution level. Listen in for Laurence’s thoughts about posttraining; the evolution of on-device AI (and how tools like ExecuTorch and LiteRT [&#8230;]]]></itunes:summary>
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	<googleplay:explicit>No</googleplay:explicit>
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<item>
	<title>Generative AI in the Real World: Chris Butler on GenAI in Product Management</title>
	<link>https://www.oreilly.com/radar/podcast/generative-ai-in-the-real-world-product-management-in-the-age-of-ai-with-chris-butler/</link>
	<pubDate>Thu, 30 Oct 2025 11:29:41 +0000</pubDate>
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	<description><![CDATA[In this episode, Ben Lorica and Chris Butler, director of product operations for GitHub&#8217;s Synapse team, chat about the experimentation Chris is doing to incorporate generative AI into the product development process—particularly with the goal of reducing toil for cross-functional teams. It isn’t just automating busywork (although there’s some of that). He and his team [&#8230;]]]></description>
	<itunes:subtitle><![CDATA[In this episode, Ben Lorica and Chris Butler, director of product operations for GitHub&#8217;s Synapse team, chat about the experimentation Chris is doing to incorporate generative AI into the product development process—particularly with the goal of re]]></itunes:subtitle>
	<content:encoded><![CDATA[In this episode, Ben Lorica and Chris Butler, director of product operations for GitHub&#8217;s Synapse team, chat about the experimentation Chris is doing to incorporate generative AI into the product development process—particularly with the goal of reducing toil for cross-functional teams. It isn’t just automating busywork (although there’s some of that). He and his team [&#8230;]]]></content:encoded>
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	<itunes:summary><![CDATA[In this episode, Ben Lorica and Chris Butler, director of product operations for GitHub&#8217;s Synapse team, chat about the experimentation Chris is doing to incorporate generative AI into the product development process—particularly with the goal of reducing toil for cross-functional teams. It isn’t just automating busywork (although there’s some of that). He and his team [&#8230;]]]></itunes:summary>
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	<googleplay:explicit>No</googleplay:explicit>
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<item>
	<title>Generative AI in the Real World: Context Engineering with Drew Breunig</title>
	<link>https://www.oreilly.com/radar/podcast/generative-ai-in-the-real-world-context-engineering-with-drew-breunig/</link>
	<pubDate>Thu, 16 Oct 2025 11:18:24 +0000</pubDate>
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	<description><![CDATA[In this episode, Ben Lorica and Drew Breunig, a strategist at the Overture Maps Foundation, talk all things context engineering: what’s working, where things are breaking down, and what comes next. Listen in to hear why huge context windows aren’t solving the problems we hoped they might, why companies shouldn’t discount evals and testing, and [&#8230;]]]></description>
	<itunes:subtitle><![CDATA[In this episode, Ben Lorica and Drew Breunig, a strategist at the Overture Maps Foundation, talk all things context engineering: what’s working, where things are breaking down, and what comes next. Listen in to hear why huge context windows aren’t solvin]]></itunes:subtitle>
	<content:encoded><![CDATA[In this episode, Ben Lorica and Drew Breunig, a strategist at the Overture Maps Foundation, talk all things context engineering: what’s working, where things are breaking down, and what comes next. Listen in to hear why huge context windows aren’t solving the problems we hoped they might, why companies shouldn’t discount evals and testing, and [&#8230;]]]></content:encoded>
	<enclosure url="https://cdn.oreillystatic.com/radar/generative-ai-real-world-podcast/GenAI_in_the_Real_World_with_Drew_Breunig.mp3" length="62180556" type="audio/mpeg"></enclosure>
	<itunes:summary><![CDATA[In this episode, Ben Lorica and Drew Breunig, a strategist at the Overture Maps Foundation, talk all things context engineering: what’s working, where things are breaking down, and what comes next. Listen in to hear why huge context windows aren’t solving the problems we hoped they might, why companies shouldn’t discount evals and testing, and [&#8230;]]]></itunes:summary>
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	<googleplay:explicit>No</googleplay:explicit>
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<item>
	<title>Generative AI in the Real World: Emmanuel Ameisen on LLM Interpretability</title>
	<link>https://www.oreilly.com/radar/podcast/generative-ai-in-the-real-world-emmanuel-ameisen-on-llm-interpretability/</link>
	<pubDate>Thu, 02 Oct 2025 14:31:22 +0000</pubDate>
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	<description><![CDATA[In this episode, Ben Lorica and Anthropic interpretability researcher Emmanuel Ameisen get into the work Emmanuel’s team has been doing to better understand how LLMs like Claude work. Listen in to find out what they’ve uncovered by taking a microscopic look at how LLMs function—and just how far the analogy to the human brain holds. [&#8230;]]]></description>
	<itunes:subtitle><![CDATA[In this episode, Ben Lorica and Anthropic interpretability researcher Emmanuel Ameisen get into the work Emmanuel’s team has been doing to better understand how LLMs like Claude work. Listen in to find out what they’ve uncovered by taking a microscopic l]]></itunes:subtitle>
	<content:encoded><![CDATA[In this episode, Ben Lorica and Anthropic interpretability researcher Emmanuel Ameisen get into the work Emmanuel’s team has been doing to better understand how LLMs like Claude work. Listen in to find out what they’ve uncovered by taking a microscopic look at how LLMs function—and just how far the analogy to the human brain holds. [&#8230;]]]></content:encoded>
	<enclosure url="https://cdn.oreillystatic.com/radar/generative-ai-real-world-podcast/GenAI_in_the_Real_World_with_Emmanuel_Ameisen.mp3" length="44040192" type="audio/mpeg"></enclosure>
	<itunes:summary><![CDATA[In this episode, Ben Lorica and Anthropic interpretability researcher Emmanuel Ameisen get into the work Emmanuel’s team has been doing to better understand how LLMs like Claude work. Listen in to find out what they’ve uncovered by taking a microscopic look at how LLMs function—and just how far the analogy to the human brain holds. [&#8230;]]]></itunes:summary>
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	<googleplay:explicit>No</googleplay:explicit>
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<item>
	<title>Generative AI in the Real World: Understanding A2A with Heiko Hotz and Sokratis Kartakis</title>
	<link>https://www.oreilly.com/radar/podcast/generative-ai-in-the-real-world-understanding-a2a-with-heiko-hotz-and-sokratis-kartakis/</link>
	<pubDate>Thu, 21 Aug 2025 13:12:41 +0000</pubDate>
	<dc:creator><![CDATA[Radar]]></dc:creator>
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	<description><![CDATA[Everyone is talking about agents: single agents and, increasingly, multi-agent systems. What kind of applications will we build with agents, and how will we build with them? How will agents communicate with each other effectively? Why do we need a protocol like A2A to specify how they communicate? Join Ben Lorica as he talks with [&#8230;]]]></description>
	<itunes:subtitle><![CDATA[Everyone is talking about agents: single agents and, increasingly, multi-agent systems. What kind of applications will we build with agents, and how will we build with them? How will agents communicate with each other effectively? Why do we need a protoc]]></itunes:subtitle>
	<content:encoded><![CDATA[Everyone is talking about agents: single agents and, increasingly, multi-agent systems. What kind of applications will we build with agents, and how will we build with them? How will agents communicate with each other effectively? Why do we need a protocol like A2A to specify how they communicate? Join Ben Lorica as he talks with [&#8230;]]]></content:encoded>
	<enclosure url="https://cdn.oreillystatic.com/radar/generative-ai-real-world-podcast/GenAI_in_the_Real_World_with_Heiko_and%20_Sokratis.mp3" length="50121932" type="audio/mpeg"></enclosure>
	<itunes:summary><![CDATA[Everyone is talking about agents: single agents and, increasingly, multi-agent systems. What kind of applications will we build with agents, and how will we build with them? How will agents communicate with each other effectively? Why do we need a protocol like A2A to specify how they communicate? Join Ben Lorica as he talks with [&#8230;]]]></itunes:summary>
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	<itunes:duration>33m 10s</itunes:duration>
	<itunes:author><![CDATA[Radar]]></itunes:author>	<googleplay:description><![CDATA[Everyone is talking about agents: single agents and, increasingly, multi-agent systems. What kind of applications will we build with agents, and how will we build with them? How will agents communicate with each other effectively? Why do we need a protocol like A2A to specify how they communicate? Join Ben Lorica as he talks with [&#8230;]]]></googleplay:description>
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	<googleplay:explicit>No</googleplay:explicit>
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<item>
	<title>Generative AI in the Real World: Jay Alammar on Building AI for the Enterprise</title>
	<link>https://www.oreilly.com/radar/podcast/generative-ai-in-the-real-world-jay-alammar-on-building-ai-for-the-enterprise/</link>
	<pubDate>Thu, 07 Aug 2025 19:00:10 +0000</pubDate>
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	<description><![CDATA[Jay Alammar, director and Engineering Fellow at Cohere, joins Ben Lorica to talk about building AI applications for the enterprise, using RAG effectively, and the evolution of RAG into agents. Listen in to find out what kinds of metadata you need when you’re onboarding a new model or agent; discover how an emphasis on evaluation [&#8230;]]]></description>
	<itunes:subtitle><![CDATA[Jay Alammar, director and Engineering Fellow at Cohere, joins Ben Lorica to talk about building AI applications for the enterprise, using RAG effectively, and the evolution of RAG into agents. Listen in to find out what kinds of metadata you need when yo]]></itunes:subtitle>
	<content:encoded><![CDATA[Jay Alammar, director and Engineering Fellow at Cohere, joins Ben Lorica to talk about building AI applications for the enterprise, using RAG effectively, and the evolution of RAG into agents. Listen in to find out what kinds of metadata you need when you’re onboarding a new model or agent; discover how an emphasis on evaluation [&#8230;]]]></content:encoded>
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	<itunes:summary><![CDATA[Jay Alammar, director and Engineering Fellow at Cohere, joins Ben Lorica to talk about building AI applications for the enterprise, using RAG effectively, and the evolution of RAG into agents. Listen in to find out what kinds of metadata you need when you’re onboarding a new model or agent; discover how an emphasis on evaluation [&#8230;]]]></itunes:summary>
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	<googleplay:explicit>No</googleplay:explicit>
	<googleplay:block>no</googleplay:block>
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	<title>Generative AI in the Real World: Phillip Carter on Where Generative AI Meets Observability</title>
	<link>https://www.oreilly.com/radar/podcast/generative-ai-in-the-real-world-phillip-carter-on-where-generative-ai-meets-observability/</link>
	<pubDate>Thu, 24 Jul 2025 10:20:18 +0000</pubDate>
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	<guid isPermaLink="false">https://www.oreilly.com/radar/?post_type=podcast&#038;p=17128</guid>
	<description><![CDATA[Phillip Carter, formerly of Honeycomb, and Ben Lorica talk about observability and AI—what observability means, how generative AI causes problems for observability, and how generative AI can be used as a tool to help SREs analyze telemetry data. There’s tremendous potential because AI is great at finding patterns in massive datasets, but it’s still a [&#8230;]]]></description>
	<itunes:subtitle><![CDATA[Phillip Carter, formerly of Honeycomb, and Ben Lorica talk about observability and AI—what observability means, how generative AI causes problems for observability, and how generative AI can be used as a tool to help SREs analyze telemetry data. There’s ]]></itunes:subtitle>
	<content:encoded><![CDATA[Phillip Carter, formerly of Honeycomb, and Ben Lorica talk about observability and AI—what observability means, how generative AI causes problems for observability, and how generative AI can be used as a tool to help SREs analyze telemetry data. There’s tremendous potential because AI is great at finding patterns in massive datasets, but it’s still a [&#8230;]]]></content:encoded>
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	<itunes:summary><![CDATA[Phillip Carter, formerly of Honeycomb, and Ben Lorica talk about observability and AI—what observability means, how generative AI causes problems for observability, and how generative AI can be used as a tool to help SREs analyze telemetry data. There’s tremendous potential because AI is great at finding patterns in massive datasets, but it’s still a [&#8230;]]]></itunes:summary>
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		<title>Generative AI in the Real World: Phillip Carter on Where Generative AI Meets Observability</title>
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	<itunes:block>yes</itunes:block>
	<itunes:duration>38m 1s</itunes:duration>
	<itunes:author><![CDATA[Radar]]></itunes:author>	<googleplay:description><![CDATA[Phillip Carter, formerly of Honeycomb, and Ben Lorica talk about observability and AI—what observability means, how generative AI causes problems for observability, and how generative AI can be used as a tool to help SREs analyze telemetry data. There’s tremendous potential because AI is great at finding patterns in massive datasets, but it’s still a [&#8230;]]]></googleplay:description>
	<googleplay:image href="https://www.oreilly.com/radar/wp-content/uploads/sites/3/2024/01/Podcast_Cover_GenAI_in_the_Real_World-scaled.png"></googleplay:image>
	<googleplay:explicit>No</googleplay:explicit>
	<googleplay:block>yes</googleplay:block>
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<item>
	<title>Generative AI in the Real World: Raiza Martin on Building AI Applications for Audio</title>
	<link>https://www.oreilly.com/radar/podcast/generative-ai-in-the-real-world-raiza-martin-on-building-ai-applications-for-audio/</link>
	<pubDate>Thu, 10 Jul 2025 19:33:18 +0000</pubDate>
	<dc:creator><![CDATA[Radar]]></dc:creator>
	<guid isPermaLink="false">https://www.oreilly.com/radar/?post_type=podcast&#038;p=17005</guid>
	<description><![CDATA[Audio is being added to AI everywhere: both in multimodal models that can understand and generate audio and in applications that use audio for input. Now that we can work with spoken language, what does that mean for the applications that we can develop? How do we think about audio interfaces—how will people use them, [&#8230;]]]></description>
	<itunes:subtitle><![CDATA[Audio is being added to AI everywhere: both in multimodal models that can understand and generate audio and in applications that use audio for input. Now that we can work with spoken language, what does that mean for the applications that we can develop?]]></itunes:subtitle>
	<content:encoded><![CDATA[Audio is being added to AI everywhere: both in multimodal models that can understand and generate audio and in applications that use audio for input. Now that we can work with spoken language, what does that mean for the applications that we can develop? How do we think about audio interfaces—how will people use them, [&#8230;]]]></content:encoded>
	<enclosure url="https://cdn.oreillystatic.com/radar/generative-ai-real-world-podcast/GenAI_in_the_Real_World_with_Raiza_Martin_1.mp3" length="54421094" type="audio/mpeg"></enclosure>
	<itunes:summary><![CDATA[Audio is being added to AI everywhere: both in multimodal models that can understand and generate audio and in applications that use audio for input. Now that we can work with spoken language, what does that mean for the applications that we can develop? How do we think about audio interfaces—how will people use them, [&#8230;]]]></itunes:summary>
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	<itunes:block>yes</itunes:block>
	<itunes:duration>36m 00s</itunes:duration>
	<itunes:author><![CDATA[Radar]]></itunes:author>	<googleplay:description><![CDATA[Audio is being added to AI everywhere: both in multimodal models that can understand and generate audio and in applications that use audio for input. Now that we can work with spoken language, what does that mean for the applications that we can develop? How do we think about audio interfaces—how will people use them, [&#8230;]]]></googleplay:description>
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	<googleplay:explicit>No</googleplay:explicit>
	<googleplay:block>yes</googleplay:block>
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