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In this episode of Code at Scale, we unpack the GitHub Engineering System Success Playbook (ESSP)—a practical, metrics-driven framework for building high-performing engineering organizations. GitHub’s ESSP reframes engineering success around the dynamic interplay of quality, velocity, and developer happiness, emphasizing that sustainable improvement comes not from isolated metrics but from system-level thinking.
We explore GitHub’s three-step improvement process—identify, evaluate, implement—and dig into the 12 core metrics across four zones (including Copilot satisfaction and AI leverage). We also highlight why leading vs. lagging indicators matter, how to avoid toxic gamification, and how to turn common engineering antipatterns into learning opportunities. Whether you're scaling a dev team or transforming engineering culture, this episode gives you the blueprint to do it with intention, impact, and empathy.
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In this episode, we unpack how generative AI is transforming the foundations of enterprise marketing. Drawing from the white paper Generative AI in Marketing: A New Era for Enterprise Marketing Strategies, we explore the rise of large language models (LLMs), diffusion models, and multimodal tools that are now driving content creation, hyper-personalization, lead scoring, dynamic pricing, and more.
From Coca-Cola’s AI-generated campaigns to JPMorgan Chase’s automated ad copy, the episode showcases real-world use cases while examining the deeper shifts in how marketing teams operate. We also confront the critical risks—data privacy, brand integrity, model bias, hallucinations—and offer strategic advice for leaders aiming to implement generative AI responsibly and at scale. If your brand is serious about leveraging AI to boost creativity, performance, and customer engagement, this is the conversation you need to hear.
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In this episode, we explore the next frontier of enterprise AI: intelligent agents empowered by the Model Context Protocol (MCP). Based on a strategic briefing from Boston Consulting Group, we trace the evolution of AI agents from simple chatbots to autonomous systems capable of planning, tool use, memory, and complex collaboration.
We dive deep into MCP, the open-source standard that's fast becoming the connective tissue of enterprise AI—enabling agents to securely access tools, query databases, and coordinate actions across environments. From real-world examples in coding and compliance to emerging security challenges and orchestration strategies, this episode lays out how companies can build secure, scalable agent systems. Whether you're deploying your first AI agent or managing an ecosystem of them, this episode maps the architecture, risks, and best practices you need to know.
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In this episode, we decode three of the most compelling architectures in the modern AI stack: Retrieval-Augmented Generation (RAG), AI Agent-Based Systems, and the cutting-edge Agentic RAG. Based on the in-depth technical briefing Retrieval, Agents, and Agentic RAG, we break down how each system works, what problems they solve, and where they shine—or struggle.
We explore how RAG grounds LLM responses with real-world data, how AI agents bring autonomy, memory, and planning into play, and how Agentic RAG fuses the two to tackle highly complex, multi-step tasks. From simple document Q&A to dynamic, multi-agent marketing strategies, this episode maps out the design tradeoffs, implementation challenges, and best practices for deploying each of these architectures. Whether you're building smart assistants, knowledge workers, or campaign bots, this is your blueprint for intelligent, scalable AI systems.
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In this episode, we explore how Large Language Models (LLMs) like GPT-4 and GitHub Copilot are revolutionizing full-stack web development—from speeding up boilerplate generation and test writing to simplifying infrastructure-as-code and DevOps workflows. Based on the white paper Enhancing Full-Stack Web Development with LLMs, we break down the tools, use cases, architectural patterns, and best practices that define modern AI-assisted development.
We cover real-world applications, including LLM-driven documentation, code refactoring, test generation, and cloud config writing. We also dive into the risks—like hallucinated code, security gaps, and over-reliance—and how to mitigate them with a human-in-the-loop approach. Whether you're a solo developer or leading a team, this episode offers a comprehensive look at the evolving toolkit for building smarter and faster with AI.
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In this episode, we dive into the nuts and bolts of MLOps—the crucial discipline that bridges the gap between machine learning development and real-world deployment. Drawing insights from Introducing MLOps by Mark Treveil and the Dataiku team, we explore what it really takes to operationalize machine learning in enterprise environments.
From building reproducible models and setting up robust CI/CD pipelines to managing data drift and enforcing responsible AI practices, we walk through the entire lifecycle of a model in production. You'll learn about the diverse roles that make MLOps successful, how to align governance with risk, and why monitoring and feedback loops are essential to long-term model health. With practical case studies in credit risk and marketing, this episode delivers a comprehensive roadmap for deploying ML systems that scale—safely, ethically, and efficiently.
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In this special episode, we unpack the major insights from the Artificial Intelligence Index Report 2025, the definitive annual report tracking AI’s global trajectory. From breakthrough advances in training efficiency and multilingual model capabilities to serious concerns about carbon emissions, bias, and ethical risks in medicine, this report gives us a sweeping view of where AI is—and where it’s heading.
We’ll dive into how AI is reshaping science, education, and the economy, discuss the exponential rise in AI patents, and explore geopolitical trends in research, talent migration, and public policy. Whether it’s massive compute powering GPT-4o, the booming generative AI investment scene, or the growing calls for responsible AI governance, this episode brings you the numbers, narratives, and nuance behind today’s AI evolution.
Expect data-backed insights, expert commentary, and a big-picture look at what it means to live in the AI age.
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In this episode, we unravel the art and science of prompt engineering—the subtle, powerful craft behind guiding large language models (LLMs) to produce meaningful, accurate, and contextually aware outputs. Drawing from the detailed guide by Lee Boonstra and her team at Google, we explore the foundational concepts of prompting, from zero-shot and few-shot techniques to advanced strategies like Chain of Thought (CoT), ReAct, and Tree of Thoughts.
We also dive into real-world applications like code generation, debugging, and translation, and explore how multimodal inputs and model configurations (temperature, top-K, top-P) affect output quality. Wrapping up with a deep dive into best practices—such as prompt documentation, structured output formats like JSON, and collaborative experimentation—you’ll leave this episode equipped to write prompts that actually work. Whether you’re an LLM pro or just starting out, this one’s packed with tips, examples, and aha moments.
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In this episode of Agents of Intelligence, we dive deep into two groundbreaking protocols shaping the future of multi-agent Large Language Model (LLM) orchestration: the Agent2Agent (A2A) Protocol and the Model Context Protocol (MCP). A2A acts as the social glue between autonomous AI agents, allowing them to communicate, delegate tasks, and negotiate how best to serve the user—almost like microservices that can think. On the other side, MCP is the information highway, standardizing how these agents access and interact with external data and tools—making sure they’re never working in isolation.
We’ll unpack the core design philosophies, key features, real-world use cases, and the powerful synergy between A2A and MCP when combined. Whether it’s onboarding a new employee or compiling a complex research report, these protocols are making it possible for intelligent agents to collaborate and operate with unprecedented depth and flexibility.
Tune in to learn how the future of AI is being built—not just with smarter models, but with smarter ways for those models to talk, think, and act together.
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In this episode, we unpack a groundbreaking new way of measuring AI capability—not by test scores, but by time. Drawing from the recent METR paper "Measuring AI Ability to Complete Long Tasks," we explore the concept of the 50% task-completion time horizon—a novel metric that asks: How long could a human work on a task before today's AI can match them with 50% success?
We’ll explore how this time-based approach offers a more intuitive and unified scale for tracking AI progress across domains like software engineering and machine learning research. The findings are eye-opening: the time horizon has been doubling roughly every seven months, suggesting we could see "one-month AI"—systems capable of reliably completing tasks that take humans 160+ hours—by 2029.
We also delve into how reliability gaps, planning failures, and context sensitivity reveal AI’s current limits, even as capabilities continue to grow exponentially. Plus, what does this mean for the future of work, safety risks, and our understanding of AGI? If you're tired of benchmark buzzwords and want to get real about how far AI has come—and how far it might go—this one's for you.
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In this episode, we dive deep into McKinsey’s March 2025 report on “The State of AI,” drawn from its global survey conducted in mid-2024. The findings reveal a world where AI—especially generative AI—is no longer in the experimental phase but is becoming embedded into the core operations of organizations across industries. We explore the rapid rise in adoption rates, the growing trend of redesigning workflows, and how larger companies are pulling ahead by centralizing governance and mitigating risk.
We also break down the role of leadership—particularly CEO involvement—in AI strategy and outcomes, discuss the challenges and opportunities in workforce reskilling, and look at the practices that separate high-impact AI implementations from the rest. Although tangible enterprise-wide EBIT impact remains elusive for many, the strategic focus on adoption, scaling, and transformation suggests that AI's full potential is just beginning to unfold.
Whether you're in tech, business leadership, or just AI-curious, this episode offers an essential snapshot of where AI is today—and where it's headed next.
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In this episode, we take a deep dive into the mathematical foundations of generative AI, unraveling the complex theories and equations that power models like VAEs, GANs, normalizing flows, and diffusion models. From linear algebra and probability to optimization and game theory, we explore the intricate math that enables AI to generate realistic images, text, and more. Whether you're an AI researcher, machine learning engineer, or just curious about how machines can dream up new realities, this episode will provide a rigorous yet engaging exploration of the formulas and concepts shaping the future of generative AI.
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Join us as we explore the cutting-edge evolution of AI agent architectures, from foundational language models to multi-modal intelligence, tool-using agents, and autonomous decision-makers. This deep technical episode breaks down the building blocks of next-generation AI systems, covering retrieval-augmented generation (RAG), memory-augmented reasoning, reinforcement learning, and multi-agent collaboration—offering AI architects, engineers, and data scientists a roadmap to designing scalable and intelligent enterprise AI.
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In this episode, we explore the hidden risks of deploying large language models (LLMs) like DeepSeek in enterprise cloud environments and the best security practices to mitigate them. Hosted by AI security experts and cloud engineers, each episode breaks down critical topics such as preventing sensitive data exposure, securing API endpoints, enforcing RBAC with Azure AD and AWS IAM, and meeting compliance standards like China’s MLPS 2.0 and PIPL. We’ll also tackle real-world AI threats like prompt injection, model evasion, and API abuse, with actionable guidance for technical teams working with Azure, AWS, and hybrid infrastructures. Whether you're an AI/ML engineer, platform architect, or security leader, this podcast will equip you with the strategies and technical insights needed to securely deploy generative AI models in the cloud.
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Welcome to Building AI at Scale, the podcast where we break down the intricacies of deploying enterprise-grade AI applications. In this series, we take a deep dive into the OpenAI Response API and explore its technical implementation, performance optimization, concurrency management, and enterprise deployment strategies. Designed for software engineers, AI architects, and data engineers, we discuss key considerations when integrating the OpenAI Python SDK with agentic frameworks like LangChain and GraphChain, as well as cloud platforms like Azure and AWS. Learn how to optimize latency, handle rate limits, implement security best practices, and scale AI solutions efficiently. Whether you’re an AI veteran or leading a new generative AI initiative in your organization, this podcast provides the technical depth and real-world insights you need to build robust AI-powered systems.
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How is AI actually being used in the workplace today? In this episode, we dive into groundbreaking research from Handa et al. (Anthropic), which analyzed over four million conversations on Claude.ai to map AI’s role in different economic tasks. The study reveals that AI is most commonly applied in software development and writing, spanning about 36% of occupations for at least a quarter of their tasks. We explore the nuances of augmentation versus automation, AI’s impact on wages and job accessibility, and what this means for the future of work. Join us for an in-depth discussion on how AI is reshaping jobs—not replacing them outright—and what the data tells us about where we’re headed next.
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As AI agents take center stage in enterprise automation, decision-making, and knowledge management, organizations must navigate a complex landscape of cloud technologies, modular architectures, and security considerations. In this episode, we dive into the insights from AI Agents in the Enterprise: Cloud-Based Solutions for Scalable Intelligence by Sam Zamany of Boston Scientific. We explore how enterprises can design and deploy intelligent, autonomous AI agents using cloud-native architectures, reusable AI components, and cutting-edge frameworks like LangChain, ReAct, and Retrieval-Augmented Generation (RAG). Through real-world case studies from companies like Morgan Stanley, Bank of America, and Moderna, we highlight the transformative power of AI agents and best practices for large-scale adoption. Whether you're an IT architect, AI practitioner, or business leader, this episode will equip you with the strategies to integrate AI agents into your enterprise ecosystem successfully.
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In this episode, we dive deep into the world of AI-driven propaganda and the alarming rise of artificial intelligence in political misinformation. From deepfake videos that put false words in politicians’ mouths to AI-powered bots that manipulate social media discourse, the spread of disinformation has never been more sophisticated. We explore real-world case studies of AI-driven political interference, including state-sponsored campaigns by Russia and China, deepfake election scandals, and the role of algorithmic manipulation in shaping public opinion. What does this mean for democracy? How can we combat AI-generated propaganda while preserving free speech? Join us as we unpack the evolving tactics of AI-powered misinformation and discuss the future of truth in the digital age.
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In this special deep-dive episode, we explore one of the most profound questions in artificial intelligence: When will AI truly think like us? Based on the research-driven book, The Future of AGI: When Will AI Think Like Us?, this episode unpacks the technical architectures, key challenges, ethical concerns, and governance frameworks shaping the development of Artificial General Intelligence. We discuss the breakthroughs needed to create AGI, the risks of misalignment, and expert predictions on when (or if) machines will achieve human-like cognition. Join us for an in-depth narrative that goes beyond the hype to examine the real science behind AGI and what it means for the future of intelligence.
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Imagine a city that thinks, learns, and adapts in real-time—where artificial intelligence manages traffic with near-zero congestion, IoT sensors monitor air quality street by street, and blockchain secures digital governance. Smart cities are no longer a futuristic vision; they are becoming a reality. But what does it take to build these sentient cities that seamlessly integrate technology, governance, and human experience?
In this podcast, we explore the end-to-end architecture of futuristic urban living—from AI-powered infrastructure to digital twins for urban planning and the cybersecurity challenges of hyper-connected spaces. We’ll break down real-world examples like Singapore, Dubai, Amsterdam, and Songdo, analyzing what works, what doesn’t, and what the future holds. Expect deep dives into AI-driven policymaking, sustainable smart grids, autonomous mobility, and the ethical dilemmas of mass urban surveillance. Whether you’re an engineer, AI enthusiast, urban planner, or simply curious about the future, this podcast will uncover the technologies shaping the cities of tomorrow.
- Visa fler
