Alexa's Input (AI)

In this episode, Alexa Griffith interviews Brady Jordan, a creative director and entrepreneur, who shares his journey from aspiring software engineer to the founder of Clip Play Media and the photo app Y2Cam. Brady discusses the intersection of creativity and technology, the importance of storytelling in video production, and the challenges of self-employment. He emphasizes the need for resilience, adaptability, and a consumer-first approach in product development, while also exploring the significance of networking and community building in achieving success.Podcast LinksWatch: ⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠Listen:⁠⁠⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠⁠⁠More Links: ⁠⁠⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠⁠⁠Find out more about the host, Alexa Griffith, at:Website: ⁠⁠⁠⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠⁠Find out more about the guest at:Website: https://www.bradyjordan.com/Chapters00:00 Introduction to Brady Jordan and His Journey06:45 The Birth of Clip Play Media14:58 Quality vs. Consistency in Content Creation24:51 Y2Cam: A Solution to Frustration30:51 Cost and Infrastructure of App Development35:30 Navigating the Challenges of Self-Employment42:51 Marketing Strategies for App Success49:04 The Value-Based Approach to Creation

Modern software is built on layers and layers of code. So how do we know we can trust it?In this episode of Alexa’s Input (AI), Alexa Griffith sits down with Justin Cappos, professor of computer science at NYU and a leading expert in software supply chain security, to unpack what trust really means in today’s digital infrastructure.From package managers and dependency chains to large-scale outages and AI systems built on inherited code, Justin explains why many security failures aren’t random accidents, they’re predictable consequences of weak process, misaligned incentives, and insecure design.They discuss:Why security only becomes visible when something breaksThe difference between unavoidable failure and negligenceHow modern software supply chains amplify small mistakesThe role of leadership and culture in preventing breachesWhy verification systems like TUF and in-toto matter more than everAs AI accelerates development and increases system complexity, the need for verifiable trust only grows. This episode is a practical look at the invisible infrastructure that keeps modern software, and increasingly, modern AI, from collapsing under its own complexity.Podcast LinksWatch: ⁠⁠⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠Listen:⁠⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠⁠More: ⁠⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠⁠Website: ⁠⁠⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠⁠Find out more about the guest at:Website: https://engineering.nyu.edu/faculty/justin-capposNYU page: https://ssl.engineering.nyu.edu/personalpages/jcappos/Wikipedia: https://en.wikipedia.org/wiki/Justin_CapposChapters00:00 Introduction to Justin Cappos and His Work01:17 The Importance of Security in Software Systems03:50 Understanding Security Breaches: Mistakes vs. System Design Problems06:34 Cultural Factors in Security Failures09:25 Justin's Journey in Software Security12:03 The Role of Academia in Enterprise Security14:10 Evaluating Enterprise Security Systems16:58 Foundational Projects in Software Security19:21 AI Security Concerns and Future Directions24:59 The Need for MCP 2.028:57 Security Challenges with LLMs32:33 Designing Secure AI Systems37:14 Ethical Dilemmas in AI Decision-Making40:17 The Role of AI in Open Source43:44 Trust and Mindset in AI Security

As AI systems grow more autonomous, the question is no longer just what they can do, but whether we can trust the data and models behind their decisions. In this episode of Alexa’s Input (AI), Alexa Griffith talks with Wendy Chin, CEO of PureCipher, about building what she calls an artificial immune system for AI, a framework designed to make data, models, and inference tamper-evident across the AI lifecycle.They unpack what data poisoning really means (training data, weights and biases, inference inputs), why small amounts of targeted poison can create outsized model misbehavior, and how generative AI lowers the barrier to sophisticated malware. The conversation expands into the security implications of agent-to-agent communication via MCP, digital twins, and why we don’t have the luxury of “shipping now and securing later.” It’s a wide-ranging discussion that moves from practical threat models to the philosophical frontier of what happens as AI becomes more human-like, and more autonomous.Podcast LinksWatch: ⁠⁠⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠Listen:⁠⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠⁠More: ⁠⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠⁠Website: ⁠⁠⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠⁠Find out more about the guest at:LinkedIn: https://www.linkedin.com/in/wendy-chin-ctg/Website: https://www.purecipher.com/Chapters00:00 Introduction to AI Security01:16 Understanding Data Poisoning04:38 The Dangers of Malware in AI07:46 AI's Moral Dilemmas and Decision Making08:45 Building Empathy in AI13:07 The Role of Good Data in AI Training17:02 PureCypher's Artificial Immune System22:34 Digital Twins and Their Implications25:22 Nurturing AI Like a Child30:53 Data Therapy for AI36:13 The Future of AI and Human Interaction38:45 The Dark Side of AI: Hacking and Security45:03 Global Perspectives on AI Security48:11 MCP Agents and Security Concerns51:41 Philosophical Implications of AI and Human Connection01:00:04 The Sci-Fi Future of AI and Humanity

As LLM apps evolve from simple chatbots to tool-using agents, the attack surface explodes, and the old security playbooks don’t hold. In this episode of Alexa’s Input (AI), Alexa Griffith sits down with Ian Webster, co-founder and CEO of PromptFoo, to break down what AI security actually looks like in practice: automated red teaming, prompt injection and jailbreak testing, evaluation workflows that scale, and why “guardrails alone” is not a security strategy.Ian shares how PromptFoo grew from a side project into a widely adopted open-source standard, what it means to raise multi-millions in a fast-moving market, and how enterprises are approaching the full vulnerability lifecycle, from finding issues to triage, remediation, and validation. Ian also discusses the “lethal trifecta” that makes agents fundamentally risky (untrusted input + sensitive data + exfil path), and why MCP security isn’t just about users and tools, it’s about dangerous tool combinations and rogue servers.Podcast LinksWatch: ⁠⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠⁠Listen:⁠⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠⁠More: ⁠⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠⁠Website: ⁠⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠Find out more about the guest at:PromptFoo Website: https://www.promptfoo.dev/Github: https://github.com/promptfoo/promptfooIan’s LinkedIn: https://www.linkedin.com/in/ianww/Chapters00:00 Introduction to AI Security Challenges02:06 Funding and Growth of PromptFu06:16 The Genesis of PromptFu11:05 Career Journey and Lessons Learned12:53 Understanding AI Red Teaming17:36 Recent AI Security Vulnerabilities19:46 The Dual Nature of AI in Security21:47 Understanding the Lethal Trifecta in AI Security24:22 Exploring Model Context Protocol (MCP) and Its Security Implications26:22 Common Security Issues in MCP Systems28:17 The Role of Identity and Permissions in AI Security30:00 Practical Implications of Using PromptFoo for Developers31:33 Evaluating Language Models: Challenges and Techniques36:34 The Limitations of Guardrails in AI Security38:25 Best Practices for Engineers in AI Development39:58 Future Trends in AI and Security42:28 Everyday Applications of AI and Language Models

Most AI infrastructure today is hitting a breaking point. Marc Austin, CEO of Hedgehog, reveals how open source networking and cloud-native solutions are revolutionizing how enterprises build and operate AI at scale. This episode addresses issues many building AI infrastructure today are facing — expensive proprietary systems, overwhelming complex network configurations, and ways to make on-prem AI infrastructure feel just like the public cloud.We discuss how networking is the hidden bottleneck in scaling GPU clusters and the surprising physics and hardware innovations enabling higher throughput. Marc shares the journey of building Hedgehog, an open source, cloud-native platform designed for AI workloads that bridges the gap between complex hardware and seamless, user-friendly cloud experiences. Marc explains how Hedgehog's software abstracts and automates the networking complexity, making AI infrastructure accessible to enterprises without dedicated networking teams.We break down the future of AI networks, from multi-cloud and hybrid environments to the rise of Neo Clouds and the open source movement transforming enterprise AI infrastructure. If you're a CTO, data scientist, or AI innovator, understanding these network innovations can be your moat. Listen to this episode to see how open source, cloud-native networking, and physical innovation are shaping the AI infrastructure of tomorrow.Podcast LinksWatch: ⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠Listen:⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠More: ⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠Website: ⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠Find out more about the guest at LinkedIn:  https://www.linkedin.com/in/austinmarc/Website: https://hedgehog.cloud/Github: https://github.com/githedgehogChapters00:00 Rethinking AI Infrastructure02:49 The Role of Networking in AI05:54 Marc's Journey to Hedgehog08:46 Lessons from Big Companies11:38 Requirements for AI Networks14:48 Advancements in AI Networking17:33 Future Challenges in AI Infrastructure20:46 Creating a Cloud Experience On-Prem23:32 The Shift to Hybrid Multi-Cloud28:10 Evolving AI Infrastructure and Efficiency30:57 AI Workloads and Network Configurations32:41 Zero Touch Lifecycle Management35:12 Support for Hardware Devices35:45 Networking Paradigms and Vendor Lock-in38:42 The Rise of Neo Clouds41:31 Demand for AI Infrastructure43:57 Open Source and Cloud-Native Networking47:27 Challenges of Building a Networking Startup50:46 Proud Accomplishments at Hedgehog52:41 Future Excitement in AI Inference

Five years ago, Kelsey Hightower helped me find my voice in tech as the guest for my fifth podcast episode. Today, the man who taught the world Kubernetes and became a legend for his live demos returns for a conversation that goes far beyond infrastructure and code.Now retired-ish, Kelsey has transitioned into a new chapter. In this episode, we explore what it means to be not only a senior engineer, but also a "senior human" in an industry obsessed with speed. Kelsey shares his unique perspective on:Real vs. Artificial Intelligence: Why we must stop ignoring real intelligence and focus on providing humans with the same context and clarity we give to AI.The Future of Engineering: Why your value will shift from writing code to making stylistic, high-impact decisions as AI levels the technical playing field.Impact Over Activity: How to stop being a "busybot" and start asking the difficult questions about why we are building in the first place.The Senior Human Unit Test: Building communities with integrity, leading with empathy, and staying balanced in a world that always wants more.Whether you are just getting into your career or a seasoned veteran, this episode is a masterclass in curiosity, craft, and the art of staying grounded while building the future.Podcast LinksWatch: ⁠⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠⁠Read: ⁠⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠⁠Listen:⁠⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠⁠More: ⁠⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠⁠Website: ⁠⁠⁠⁠https://alexagriffith.com/⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠⁠Find out more about the guest at:Bluesky: https://bsky.app/profile/kelseyhightower.comLinkedIn: https://www.linkedin.com/in/kelsey-hightower-849b342b1GitHub Profile: https://github.com/kelseyhightowerKubernetes the Hard Way: https://github.com/kelseyhightower/kubernetes-the-hard-wayNo Code (The minimalist project): https://github.com/kelseyhightower/nocodeKubernetes: Up and Running (Book): https://www.oreilly.com/library/view/kubernetes-up-and/9781492046523/Chapters00:00 Introduction and Background01:10 Transitioning from Engineer to Tech Philosopher04:00 The Importance of Being a Senior Human07:23 AI's Impact on People Skills10:12 The Future of Engineering in an AI World15:04 Navigating the AI Shift21:21 Finding Impact Over Activity25:47 Creating Meaningful Products29:57 The Power of Listening and Connection35:21 The Importance of Listening in Discussions35:55 Embracing the Learning Journey36:58 Understanding Imposter Syndrome39:33 Creating Supportive Learning Environments40:31 Learning in Public and Sharing Experiences41:31 Finding Your Own Voice43:26 The Power of Emotion in Presentations47:29 Crafting Engaging Stories48:40 Improvisation in Public Speaking55:10 The Evolution of Presentation Styles01:03:28 Legacy and Impact in the Tech Community

In a deep dive with Joe Beda, co-creator of Kubernetes and a cloud-native visionary, listeners explore the fascinating journey from tech giant to startup. Joe shares the origin story of Kubernetes and sheds light on the nuances of open source, detailing how infrastructure success can bear personal costs. He discusses the delicate balance of fast shipping versus quality, the role of engineering culture, and insights on work-life balance and burnout. With candid anecdotes, Joe advocates for purposeful work and collaborative success.

Why do so many AI rollouts stall right after the tools ship?In this episode of Alexa’s Input (AI), Alexa talks with Melissa Reeve, author of the book Hyper Adaptive: Rewiring the Enterprise to Become AI Native, about what it actually takes to get AI adopted in large organizations.Melissa shares how her background in lean, Agile, and DevOps transformation shaped her view that AI adoption is less about “buying the tool” and more about rewiring how work happens. Together, they break down why many AI initiatives fail (and why ROI is slow), the FOCUS framework, the “AI time paradox,” and how support structures like AI activation hubs, social learning, and better success metrics can raise quality and accelerate impact.A must-listen for engineering leaders, product teams, and executives trying to move beyond pilots and turn AI into real operational leverage.Learn more about Melissa and Hyper Adaptive below.LinksWatch: ⁠⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠⁠Read: ⁠⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠⁠Listen:⁠ https://creators.spotify.com/pod/profile/alexagriffith/⁠More: ⁠⁠⁠https://linktr.ee/alexagriffith⁠⁠⁠Website: ⁠⁠⁠https://alexagriffith.com/⁠⁠⁠LinkedIn: ⁠⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠⁠Find out more about the guest at:LinkedIn: https://www.linkedin.com/in/melissamreeve/Book: https://itrevolution.com/product/hyperadaptive/KeywordsAI adoption, enterprise transformation, Hyper Adaptive model, organizational change, DevOps, Lean, Agile, AI integration, customer-centricity, innovation accounting, social learningChapters00:00 Introduction to AI Adoption in Enterprises03:00 Melissa's Journey and the Foundation of AI Thinking06:06 The Analogy of DevOps and AI Implementation08:47 Cultural Shifts vs. Tooling in AI Adoption11:49 The Hyper Adaptive Model for AI Integration14:48 Sociology of Workflows and Organizational Change17:49 Understanding AI Initiative Failures21:00 Customer Centricity in AI Solutions23:58 The AI Time Paradox and Learning26:58 AI Activation Hubs and Their Role30:54 The Role of Human Oversight in AI Automation34:03 Incentivizing AI Engagement in Organizations35:59 Social Learning and AI: The Power of Collaboration40:57 Practical Applications of AI in Daily Life44:44 Quality vs. Productivity: The AI Dilemma46:13 The Focus Framework: Prioritizing AI Use Cases48:23 Influencing AI Adoption in Organizations51:07 The Future of Hyper Adaptive Organizations55:08 Decision-Making in the Age of AI57:37 Key Takeaways for Leaders in the AI Revolution

What does it actually take to move machine learning from experiments into production reliably, responsibly, and at scale?In this episode of Alexa’s Input (AI), Alexa talks with Maria Vechtomova, co-founder of Marvelous MLOps and an O’Reilly author-in-progress on MLOps with Databricks. Maria shares how her background in data science led her into MLOps, and why most teams struggle not because of tools, but because of missing processes, traceability, and shared understanding across teams.Alexa and Maria dive into what separates good MLOps from fragile deployments, why shipping notebooks as “production” creates long-term pain, and how traceability across code, data, and environment forms the foundation for reliable ML systems. They also explore how LLM applications are reshaping MLOps tooling, and where the biggest skill gaps still exist between platform, data, and AI engineers.A must-listen for anyone building, operating, or scaling machine learning systems and for teams trying to make MLOps less magical and more marvelous.Learn more about Marvelous MLOps and Maria’s work below.LinksWatch: ⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠Read: ⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠Listen: https://creators.spotify.com/pod/profile/alexagriffith/More: ⁠⁠https://linktr.ee/alexagriffith⁠⁠Website: ⁠⁠https://alexagriffith.com/⁠⁠LinkedIn: ⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠Find out more about the guest at:LinkedIn: https://www.linkedin.com/in/maria-vechtomova/TakeawaysMaria started as a data analyst and transitioned into MLOps.She emphasizes the importance of tracking data, code, and environment in MLOps.MLOps is a practice to bring machine learning models to production reliably.Good deployment processes require modular code and proper tracking.MLOps differs from DevOps due to the complexities of data and model drift.Education is crucial for bridging gaps between teams in AI.Small steps can lead to better MLOps practices.Scaling MLOps requires understanding the unique data of different brands.The rise of LLMs is changing the MLOps landscape.Effective teaching methods involve step-by-step guidance.Chapters00:00 Introduction to MLOps and Maria's Journey02:11 Maria's Path to MLOps and Knowledge Sharing04:41 The Importance of MLOps in AI Deployments10:12 Defining MLOps and Its Challenges11:38 MLOps vs. DevOps: Key Differences13:00 Overcoming Stagnation in MLOps16:04 Small Steps Towards Better MLOps Practices19:29 Scaling MLOps in Large Organizations21:58 The Impact of LLMs on MLOps23:58 The Shift from Traditional ML to AI Applications26:51 Evolving Roles in AI Engineering28:33 Databricks: A Comprehensive AI Platform31:45 Future of AI Platforms and Regulations34:26 Bridging Skill Gaps in AI Teams38:42 The Importance of Context in AI Development40:40 Foundational Skills for MLOps Professionals45:43 Integrating Personal Passions with Professional Growth47:30 Building Impactful AI Communities

What if your building was smarter and optimized energy, security, and tenant experience in the background?In this episode of Alexa’s Input (AI), Alexa talks with Aaron Short, founder and CEO of B-Line, an all-in-one facility management platform that uses AI to automate building operations. Aaron shares how his early work in urban planning and green building led him to build an “operating system for buildings” that connects siloed systems like access control, visitor management, emergency response, work orders, smart controls, and tenant support, without ripping and replacing legacy infrastructure.Alexa and Aaron dive into how AI agents power 24/7 customer service for property managers, reduce the need for on-site security through biometric access and digital IDs, and use occupancy and sensor data to drive real energy savings and predictive maintenance. They also explore why real estate has been historically slow to digitize, how to win in a legacy-heavy space, and where AI is having the biggest impact across proptech and climate tech.A must listen for anyone interested in the intersection of tech, real estate, urban planning, and building management! Learn more about BLine at b-line.io.LinksAaron’s LinkedIn: https://www.linkedin.com/in/aaron-short-66213641/B-Line’s Website: https://www.b-line.ioListen, watch, and read more about this podcast atAlexa’s Input YouTube Channel: https://www.youtube.com/@alexa_griffithLinkTree: https://linktr.ee/alexagriffithWebsite: https://alexagriffith.com/LinkedIn: https://www.linkedin.com/in/alexa-griffith/Substack: ⁠https://alexasinput.substack.com/⁠Chapters00:00 Introduction to Smart Buildings and AI02:58 Aaron Short's Journey to Founding Beeline05:18 The Role of Data in Urban Planning and Green Building06:22 Transitioning from Employee to Founder08:52 Overview of Beeline and Its Solutions11:41 AI Integration in Building Management13:42 The Smart Building Tech Landscape16:07 Differentiators in the Smart Building Space18:18 AI's Impact on Energy Management and Security20:37 The Future of Smart Buildings and Design22:52 Advice for Founders in Slow-Moving Industries23:45 Resilience and Learning from Setbacks26:10 Personal Reflections on Founding Beeline30:08 general_outro.wav