Today, we’re joined by Zach Wasserman, Co-Founder of Fleet, open-source device management for IT and security teams with thousands of laptops and servers. We talk about:  Best ways to build trust with usersImpacts of AI on open source, including using gen AI to describe human-created queriesCross-platform endpoint managementDetermining the scope of device management with BYOD & less traditional computing devicesDevice management surprises

Learn to use Managed Identities in Dataverse plugins to safely authenticate with Azure endpoints, without secrets. From a new Dataverse and Azure environment to a modern secure integration, you will be able to update your code to the latest security standards.

The book explores the fundamental principles and transformative advancements in cutting-edge algorithmic technologies, detailing their application and impact on revolutionizing healthcare. This book provides an in-depth account of how technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are reshaping healthcare, transitioning from traditional diagnostic and treatment approaches to data-driven solutions that improve predictive accuracy and patient outcomes. The text also addresses the challenges and considerations associated with adopting these technologies, including ethical implications, data security concerns, and the need for human-centered approaches in algorithmic medicine. After introducing digital twin technology and its potential to enhance healthcare delivery, the book examines the broader effects of digital technology on the healthcare system. Subsequent chapters explore topics such as innovations in medical imaging, predictive analytics for improved patient outcomes, and deep learning algorithms for brain tumor detection. Other topics include generative adversarial networks (GANs), convolutional neural networks (CNNs), smart wearables for remote patient monitoring, effective IoT solutions, telemedicine advancements, and blockchain security for healthcare systems. The integration of biometric systems driven by AI, securing cyber-physical systems in healthcare, and digitizing wellness through electronic health records (EHRs) and electronic medical records (EMRs) are also discussed. The book concludes with an extensive case study comparing the impacts of various healthcare applications, offering insights and encouraging further research and innovation in this dynamic field. Audience This book is suitable for academicians and professionals in health informatics, bioinformatics, biomedical science and engineering, artificial intelligence, as well as clinicians, IT specialists, and policymakers in healthcare.

Deepti Srivastava, Founder of Snow Leopard AI and former Spanner Product Lead at Google Cloud, joined Yuliia to chat what's wrong with current approaches to AI integration. Deepti introduces a paradigm shift away from ETL pipelines towards federated, real-time data access for AI applications. She explains how Snow Leopard's intelligent data retrieval platform enables enterprises to connect AI systems directly to operational data sources without compromising security or freshness. Through practical examples Deepti explains why conventional RAG approaches with vector stores are not good enough for business-critical AI applications, and how a systems thinking approach to AI infrastructure can unlock greater value while reducing unnecessary data movement.Deepti's linkedin - https://www.linkedin.com/in/thedeepti/Snowleopard.ai - http://snowleopard.ai/

When it comes to patching in Operational Technology (OT) environments, the decision isn’t always straightforward. Should you patch immediately, wait for the next planned maintenance cycle, or take a different approach? These are the questions OT professionals face every day as they balance cyber security, operational uptime, and risk management.

By exploring practical strategies and real-world challenges, this presentation will guide you through an effective patch management program. You’ll walk away with actionable insights to strengthen your cyber security posture while maintaining operational efficiency. We will cover:

• Regulatory Guidance on Patching: Understanding compliance requirements like NERC CIP and CISA.
• Challenges in Patching OT Systems: Overcoming obstacles such as asset diversity, vendor approvals, and limited resources.
• The Virtuous Loop (VL): A continuous process for effective patch and vulnerability management.
• Key Challenges of the Virtuous Loop: Addressing complexities in asset inventory, prioritization, and validation.
• Asset Consolidation: Techniques for identifying, categorizing, and contextualizing assets.
• Patch & Vulnerability Identification: Managing the overwhelming volume of patch and vulnerability data.
• Patch Now, Next, Never?: Strategic decision-making for prioritizing patching efforts.
• Remediation Identification: Exploring mitigation options like patch deployment, hardening, and virtual patching.
• Demystifying Virtual Patching: When and how to use temporary mitigation strategies effectively.
• Tracking Execution & SLAs: Monitoring progress, ensuring compliance, and optimizing patch management programs.
• Key Takeaways & Best Practices: Practical insights for building resilient OT cyber security strategies.

This webinar provides practical guidance and real-world solutions to help organizations secure critical infrastructure, reduce risk, and achieve compliance.

When it comes to patching in Operational Technology (OT) environments, the decision isn’t always straightforward. Should you patch immediately, wait for the next planned maintenance cycle, or take a different approach? These are the questions OT professionals face every day as they balance cyber security, operational uptime, and risk management.

By exploring practical strategies and real-world challenges, this presentation will guide you through an effective patch management program. You’ll walk away with actionable insights to strengthen your cyber security posture while maintaining operational efficiency. We will cover:

• Regulatory Guidance on Patching: Understanding compliance requirements like NERC CIP and CISA.
• Challenges in Patching OT Systems: Overcoming obstacles such as asset diversity, vendor approvals, and limited resources.
• The Virtuous Loop (VL): A continuous process for effective patch and vulnerability management.
• Key Challenges of the Virtuous Loop: Addressing complexities in asset inventory, prioritization, and validation.
• Asset Consolidation: Techniques for identifying, categorizing, and contextualizing assets.
• Patch & Vulnerability Identification: Managing the overwhelming volume of patch and vulnerability data.
• Patch Now, Next, Never?: Strategic decision-making for prioritizing patching efforts.
• Remediation Identification: Exploring mitigation options like patch deployment, hardening, and virtual patching.
• Demystifying Virtual Patching: When and how to use temporary mitigation strategies effectively.
• Tracking Execution & SLAs: Monitoring progress, ensuring compliance, and optimizing patch management programs.
• Key Takeaways & Best Practices: Practical insights for building resilient OT cyber security strategies.

This webinar provides practical guidance and real-world solutions to help organizations secure critical infrastructure, reduce risk, and achieve compliance.

This event is sponsored by Foxguard

Supported by Our Partners • Swarmia — The engineering intelligence platform for modern software organizations. • Graphite — The AI developer productivity platform.  • Vanta — Automate compliance and simplify security with Vanta. — On today’s episode of The Pragmatic Engineer, I’m joined by Chip Huyen, a computer scientist, author of the freshly published O’Reilly book AI Engineering, and an expert in applied machine learning. Chip has worked as a researcher at Netflix, was a core developer at NVIDIA (building NeMo, NVIDIA’s GenAI framework), and co-founded Claypot AI. She also taught Machine Learning at Stanford University. In this conversation, we dive into the evolving field of AI Engineering and explore key insights from Chip’s book, including: • How AI Engineering differs from Machine Learning Engineering  • Why fine-tuning is usually not a tactic you’ll want (or need) to use • The spectrum of solutions to customer support problems – some not even involving AI! • The challenges of LLM evals (evaluations) • Why project-based learning is valuable—but even better when paired with structured learning • Exciting potential use cases for AI in education and entertainment • And more! — Timestamps (00:00) Intro  (01:31) A quick overview of AI Engineering (05:00) How Chip ensured her book stays current amidst the rapid advancements in AI (09:50) A definition of AI Engineering and how it differs from Machine Learning Engineering  (16:30) Simple first steps in building AI applications (22:53) An explanation of BM25 (retrieval system)  (23:43) The problems associated with fine-tuning  (27:55) Simple customer support solutions for rolling out AI thoughtfully  (33:44) Chip’s thoughts on staying focused on the problem  (35:19) The challenge in evaluating AI systems (38:18) Use cases in evaluating AI  (41:24) The importance of prioritizing users’ needs and experience  (46:24) Common mistakes made with Gen AI (52:12) A case for systematic problem solving  (53:13) Project-based learning vs. structured learning (58:32) Why AI is not the end of engineering (1:03:11) How AI is helping education and the future use cases we might see (1:07:13) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Applied AI Software Engineering: RAG https://newsletter.pragmaticengineer.com/p/rag  • How do AI software engineering agents work? https://newsletter.pragmaticengineer.com/p/ai-coding-agents  • AI Tooling for Software Engineers in 2024: Reality Check https://newsletter.pragmaticengineer.com/p/ai-tooling-2024  • IDEs with GenAI features that Software Engineers love https://newsletter.pragmaticengineer.com/p/ide-that-software-engineers-love — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Send us a text Welcome back to Making Data Simple, where we explore how data-driven strategies ignite innovation and transform businesses. In this exciting episode, we sit down with Marco Rota, VP of Strategic Technology Alliances at Lumen Technologies, whose incredible journey spans from the glitz of Hollywood to leading-edge telecommunications. Tune in as Marco reveals how embracing a vibrant culture, drawing on lessons from the entertainment industry, and championing new technologies can propel teams and organizations to new heights of success. Get ready for an inspiring, behind-the-scenes look at how “culture eats strategy for breakfast”—and why that’s a game-changer for your organization, too! 01:47 – Meet Marco Rota Marco shares his background and how his career path took him from the dynamic world of Hollywood to a leadership role at Lumen Technologies. He underscores his passion for storytelling, collaboration, and innovation—elements that continue to shape his work in tech.03:35 – Learnings from Hollywood Drawing on Hollywood’s fast-paced environment, Marco highlights the importance of creative thinking and adaptability. He explains how these traits help push organizations to stay ahead of disruption and continually evolve, just like the film industry does to meet audience demands.10:56 – Transitioning to Lumen Technologies Marco describes his shift from entertainment into the telecommunications and technology space. He emphasizes the parallels between Hollywood and tech—both thrive on communication, audience engagement, and cutting-edge production processes.15:55 – What IS Lumen Technologies Marco explains Lumen’s core mission: powering next-generation connectivity, cloud, edge computing, and security solutions. By marrying technology services with an innovative culture, Lumen seeks to help organizations accelerate data-driven transformation.18:29 – Culture versus Technology An organization’s culture can be its greatest asset—or its biggest hurdle. Culture “eats strategy for breakfast” because fostering collaboration, trust, and continuous learning is what truly drives successful technology initiatives forward.24:20 – The Management System Marco talks about the framework for leadership and team alignment at Lumen, which integrates vision, purpose, and measurable goals. This system ensures that cultural values and strategic objectives reinforce each other—resulting in cohesive, energized teams ready to tackle the biggest challenges in tech.Linkedin: linkedin.com/in/marcorotapix Website: https://www.lumen.com/en-us/home.html Want to be featured as a guest on Making Data Simple?  Reach out to us at [email protected] and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

MakingDataSimple #CultureEatsStrategy #DataInnovation #DigitalTransformation #TechLeadership #PodcastEpisode #HollywoodToTech #LumenTechnologies #BusinessInsights #Inspiration

Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

The rise of AI agents in the workplace is transforming how businesses operate, tackling repetitive tasks and freeing up human employees for more creative endeavors. But what does this mean for the future of work, and how can professionals leverage these tools effectively? As AI agents become more sophisticated, capable of reasoning and decision-making, how do you ensure they align with your business goals? What are the implications for data privacy and security, and how do you manage the transition to a more automated workforce while maintaining human oversight? Surojit Chatterjee is the founder and CEO of Ema. Previously, he guided Coinbase through a successful 2021 IPO as its Chief Product Officer and scaled Google Mobile Ads and Google Shopping into multi-billion dollar businesses as the VP and Head of Product. Surojit holds 40 US patents and has an MBA from MIT, MS in Computer Science from SUNY at Buffalo, and B. Tech from IIT Kharagpur. In the episode, Richie and Surojit explore the transformative role of AI agents in automating repetitive business tasks, enhancing creativity and innovation, improving customer support, and redefining workplace efficiency. They discuss the potential of AI employees, data privacy concerns, and the future of AI-driven business processes, and much more. Links Mentioned in the Show: EmaConnect with SurojitSkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: How Generative AI is Changing Leadership with Christie Smith, Founder of the Humanity Institute and Kelly Monahan, Managing Director, Research InstituteAttend RADAR Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Bogdan Banu, Data Engineering Manager at Veed.io, joined Yuliia to share his journey of building a data platform from scratch at a fast-growing startup. As Veed's first data hire, Bogdan discusses how he established a modern data stack while maintaining strong governance principles and cost consciousness. Bogdan covered insights on implementing consent-based video data processing for AI initiatives, approaches to data democratization, and how his data team balancs velocity with security. Bogdan shared his perspectives on making strategic vendor choices, measuring business value, and fostering a culture of intelligent experimentation in startup environments.Bogdan's Linkedin - https://www.linkedin.com/in/bogdan-banu-a68a237/

Supported by Our Partners • Formation — Level up your career and compensation with Formation.  • WorkOS — The modern identity platform for B2B SaaS • Vanta — Automate compliance and simplify security with Vanta. — In today’s episode of The Pragmatic Engineer, I’m joined by Jonas Tyroller, one of the developers behind Thronefall, a minimalist indie strategy game that blends tower defense and kingdom-building, now available on Steam. Jonas takes us through the journey of creating Thronefall from start to finish, offering insights into the world of indie game development. We explore: • Why indie developers often skip traditional testing and how they find bugs • The developer workflow using Unity, C# and Blender • The two types of prototypes game developers build  • Why Jonas spent months building game prototypes in 1-2 days • How Jonas uses ChatGPT to build games • Jonas’s tips on making games that sell • And more! — Timestamps (00:00) Intro (02:07) Building in Unity (04:05) What the shader tool is used for  (08:44) How a Unity build is structured (11:01) How game developers write and debug code  (16:21) Jonas’s Unity workflow (18:13) Importing assets from Blender (21:06) The size of Thronefall and how it can be so small (24:04) Jonas’s thoughts on code review (26:42) Why practices like code review and source control might not be relevant for all contexts (30:40) How Jonas and Paul ensure the game is fun  (32:25) How Jonas and Paul used beta testing feedback to improve their game (35:14) The mini-games in Thronefall and why they are so difficult (38:14) The struggle to find the right level of difficulty for the game (41:43) Porting to Nintendo Switch (45:11) The prototypes Jonas and Paul made to get to Thronefall (46:59) The challenge of finding something you want to build that will sell (47:20) Jonas’s ideation process and how they figure out what to build  (49:35) How Thronefall evolved from a mini-game prototype (51:50) How long you spend on prototyping  (52:30) A lesson in failing fast (53:50) The gameplay prototype vs. the art prototype (55:53) How Jonas and Paul distribute work  (57:35) Next steps after having the play prototype and art prototype (59:36) How a launch on Steam works  (1:01:18) Why pathfinding was the most challenging part of building Thronefall (1:08:40) Gen AI tools for building indie games  (1:09:50) How Jonas uses ChatGPT for editing code and as a translator  (1:13:25) The pros and cons of being an indie developer  (1:15:32) Jonas’s advice for software engineers looking to get into indie game development (1:19:32) What to look for in a game design school (1:22:46) How luck figures into success and Jonas’s tips for building a game that sells (1:26:32) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Game development basics https://newsletter.pragmaticengineer.com/p/game-development-basics  • Building a simple game using Unity https://newsletter.pragmaticengineer.com/p/building-a-simple-game — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Season 1 Episode 29: Navigating Trade-Offs and Balancing Priorities The Data Product Management In Action podcast, brought to you by executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. In this episode of Data Product Management in Action, host Alexa Westlake talks with Anita Chen, diving into the complexities of managing data products. Anita, a product manager at PagerDuty, shares her approach to defining data products, prioritizing work, and balancing project work with interrupt-driven tasks. They discuss the critical roles of governance, security, and user enablement while emphasizing the importance of transparency and communication. The conversation also explores the transformative potential of generative AI in data product interactions and the build-vs-buy decision-making process. Gain insights into how data product management uniquely differs from traditional software product management and learn actionable strategies for success. Meet our Host Alexa Westlake: Alexa is a Data Analytics Leader in the Identity and Access Management space with a proven track record scaling high-growth SaaS companies. As a Staff Data Analyst at Okta, she brings a wealth of expertise in enterprise data, business intelligence, and strategic decision-making from the various industries she's worked in including telecommunications, strategy execution, and cloud computing. With a passion for harnessing the power of data for actionable insights, Alexa plays a crucial role in driving Okta's security, growth, and scale, helping the organization leverage data to execute on their market opportunity. Connect with Alexa on LinkedIn.

Meet our guest Anita Chen:  Anita is a Data Product Manager at PagerDuty, a digital operations company helping teams resolve issues faster, eliminate alert fatigue, and build more reliable services! Her background is mainly in the People Analytics space which has now expanded to data at scale with our Enterprise Data Team. She currently helps build data products that enable our teams to deliver the best possible customer experience. Anita is most passionate about how data can impact someone's lived experience and endeavor to democratize data in everything she builds. Connect with Anita on LinkedIn. All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else.  Join the conversation on LinkedIn.  Apply to be a guest or nominate someone that you know.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights! 

The Data Product Management In Action podcast, brought to you by  executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. In Season 01, Episode 28, we are excited to introduce to you a new host, Alexa Westgate! Join us as we learn all about her data journey. She'll discuss how she got into DPM, some of her greatest moments and challenges. You'll be excited for her future episodes! About our host Alexa Westlake: Alexa is a Data Analytics Leader in the Identity and Access Management space with a proven track record scaling high-growth SaaS companies. As a Staff Data Analyst at Okta, she brings a wealth of expertise in enterprise data, business intelligence, and strategic decision-making from the various industries she's worked in including telecommunications, strategy execution, and cloud computing. With a passion for harnessing the power of data for actionable insights, Alexa plays a crucial role in driving Okta's security, growth, and scale, helping the organization leverage data to execute on their market opportunity. Connect with Alexa on LinkedIn. All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else.  Join the conversation on LinkedIn.  Apply to be a guest or nominate someone that you know.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights! 

Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimization for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimization using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning. You’ll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they’re put into action. About the Technology Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods. About the Book Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You’ll especially appreciate author Vadim Smolyakov’s clear interpretations of Bayesian algorithms for Monte Carlo and Markov models. What's Inside Monte Carlo stock price simulation EM algorithm for hidden Markov models Imbalanced learning, active learning, and ensemble learning Bayesian optimization for hyperparameter tuning Anomaly detection in time-series About the Reader For machine learning practitioners familiar with linear algebra, probability, and basic calculus. About the Author Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. Quotes I love this book! It shows you how to implement common ML algorithms in plain Python with only the essential libraries, so you can see how the computation and math works in practice. - Junpeng Lao, Senior Data Scientist at Google I highly recommend this book. In the era of ChatGPT real knowledge of algorithms is invaluable. - Vatsal Desai, InfoDesk Explains algorithms so well that even a novice can digest it. - Harsh Raval, Zymr

Supported by Our Partners • Sonar —  Trust your developers – verify your AI-generated code. • Vanta —Automate compliance and simplify security with Vanta. — In today's episode of The Pragmatic Engineer, I'm joined by Charity Majors, a well-known observability expert – as well as someone with strong and grounded opinions. Charity is the co-author of "Observability Engineering" and brings extensive experience as an operations and database engineer and an engineering manager. She is the cofounder and CTO of observability scaleup Honeycomb. Our conversation explores the ever-changing world of observability, covering these topics: • What is observability? Charity’s take • What is “Observability 2.0?” • Why Charity is a fan of platform teams • Why DevOps is an overloaded term: and probably no longer relevant • What is cardinality? And why does it impact the cost of observability so much? • How OpenTelemetry solves for vendor lock-in  • Why Honeycomb wrote its own database • Why having good observability should be a prerequisite to adding AI code or using AI agents • And more! — Timestamps (00:00) Intro  (04:20) Charity’s inspiration for writing Observability Engineering (08:20) An overview of Scuba at Facebook (09:16) A software engineer’s definition of observability  (13:15) Observability basics (15:10) The three pillars model (17:09) Observability 2.0 and the shift to unified storage (22:50) Who owns observability and the advantage of platform teams  (25:05) Why DevOps is becoming unnecessary  (27:01) The difficulty of observability  (29:01) Why observability is so expensive  (30:49) An explanation of cardinality and its impact on cost (34:26) How to manage cost with tools that use structured data  (38:35) The common worry of vendor lock-in (40:01) An explanation of OpenTelemetry (43:45) What developers get wrong about observability  (45:40) A case for using SLOs and how they help you avoid micromanagement  (48:25) Why Honeycomb had to write their database  (51:56) Companies who have thrived despite ignoring conventional wisdom (53:35) Observability and AI  (59:20) Vendors vs. open source (1:00:45) What metrics are good for  (1:02:31) RUM (Real User Monitoring)  (1:03:40) The challenges of mobile observability  (1:05:51) When to implement observability at your startup  (1:07:49) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • How Uber Built its Observability Platform https://newsletter.pragmaticengineer.com/p/how-uber-built-its-observability-platform  • Building an Observability Startup https://newsletter.pragmaticengineer.com/p/chronosphere  • How to debug large distributed systems https://newsletter.pragmaticengineer.com/p/antithesis  • Shipping to production https://newsletter.pragmaticengineer.com/p/shipping-to-production  — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Kirk is joined once again by Mike Sarraille, CEO and Founder of Talent War Group (recently acquired by Overwatch) to discuss his transition to the data center industry, the critical need for effective leadership training, particularly in the face of national security threats brought on by AI and technological demands, and the necessity of innovative approaches to leadership training to prepare the next generation of leaders.

0:00 Welcome to the Data Center World 2:12 The Excitement of Transition 6:00 Evolved Leadership in the Data Center 8:51 The Need for Leadership Training 14:07 The Role of Veterans in Industry 17:04 Understanding the Data Center's Impact 19:10 The Future of AI and Data Centers 22:07 National Security and Industry Growth 35:37 The Conversation Shifts to Nuclear Energy 43:27 China's Nuclear Advancements and Global Impact 51:49 The Long-term View of Global Relations 53:47 Preparing the Next Generation for Change 56:57 Technology's Unintended Consequences 59:52 The New Age of Warfare 1:00:06 The Evolution of Education 1:03:38 The Fifth Industrial Revolution 1:06:13 The Chinese Education System 1:13:07 Ethics in Warfare 1:14:42 The Impact of Social Media 1:22:00 AI and Job Opportunities 1:25:32 The Future of Leadership 1:30:13 The Military's Role in Society 1:32:57 The Need for Adaptability 1:44:49 The Generational Shift 1:51:30 Data Centers and National Security

For more about us: https://linktr.ee/overwatchmissioncritical

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. In this episode, we delve into the big topics shaping our digital landscape: Car Expo - Brussels Motor Show: Highlights from Europe’s leading auto show, including Tesla’s Cybertruck debut and an innovative AI-powered car configurator that personalizes your vehicle experience.Biden Admin’s New AI Chip Export Rules: Exploring restrictions aimed at national security and their impact on global markets, with industry reactions from Nvidia.Meta and Microsoft’s AI Development Plans: From Meta’s goal to replace mid-level engineers with AI to Microsoft forming a dev-focused AI organization, we unpack their strategies and implications.Developer Productivity in 2025: How AI tools are changing workflows, boosting efficiency, and introducing new challenges.UV’s Killer Feature: Discover how ad-hoc environments are transforming development, courtesy of Lukas Valatka's insights.Doom in a PDF: Yes, you read that right—Doom running inside a PDF! Here’s the source code for all the geeks out there.Marimo: An exciting new project redefining collaborative development.AI and Everyday Life: A witty meme highlights AI’s direction—should it help with art and writing, or chores like laundry and dishes?

Supported by Our Partners • Vanta — Automate compliance and simplify security with Vanta. • WorkOS — The modern identity platform for B2B SaaS. — In today’s episode of The Pragmatic Engineer, I’m joined by Michael Novati, Co-founder and CTO of Formation. Before launching Formation, Michael spent eight years at Meta, where he was recognized as the top code committer company-wide for several years. The “Coding Machine” archetype was modeled after Michael at the company. In our conversation, we talk about what it was like working at Meta and dive into its engineering culture. Michael shares his journey of quickly climbing the ranks from intern to principal-level and gives level-headed advice on leveling up your career. Plus, we discuss his work at Formation, where he helps engineers grow and land roles at top tech companies. In this episode, we cover: • An overview of software architect archetypes at Meta, including “the coding machine” • Meta’s org structure, levels of engineers, and career trajectories • The importance of maintaining a ‘brag list’ to showcase your achievements and impact • Meta’s engineering culture and focus on building internal tools • How beating Mark Zuckerberg in a game of Risk led to him accepting Michael’s friend request • An inside look at Meta’s hiring process • Tips for software engineers on the job market on how to do better in technical interviews • And more! — Timestamps (00:00) Intro (01:45) An explanation of archetypes at Meta, including “the coding machine” (09:14) The organizational structure and levels of software engineers at Meta (10:05) Michael’s first project re-writing the org chart as an intern at Meta (12:42) A brief overview of Michael’s work at Meta  (15:29) Meta’s engineering first culture and how Michael pushed for even more for ICs (20:03) How tenure at Meta correlated with impact  (23:47) How Michael rose through the ranks at Meta so quickly (29:30) The engineering culture at Meta, including how they value internal tools (34:00) Companies that began at Meta or founded by former employees (36:11) Facebook’s internal tool for scheduling meetings  (37:45) The product problems that came with scaling Facebook (39:25) How Michael became Facebook friends with Mark Zuckerberg  (42:05) The “Zuck review” process (44:30) How the French attacks crashed Michael’s photo inlay prototype (51:15) How the photo inlay bug was fixed  (52:58) Meta’s hiring process  (1:03:40) Insights from Michael’s work at Formation (1:09:08) Michael’s advice for experienced engineers currently searching for a job (1:11:15) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Inside Meta’s engineering culture: https://newsletter.pragmaticengineer.com/p/facebook • Stacked diffs (and why you should know about them) https://newsletter.pragmaticengineer.com/p/stacked-diffs  • Engineering career paths at Big Tech and scaleups: https://newsletter.pragmaticengineer.com/p/engineering-career-paths  • Inside the story of how Meta built the Threads app: https://newsletter.pragmaticengineer.com/p/building-the-threads-app — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Supported by Our Partners • DX — DX is an engineering intelligence platform designed by leading researchers.  • Vanta — Automate compliance and simplify security with Vanta. — In today’s episode of The Pragmatic Engineer, I catch up with one of the best tech recruiters I’ve had the opportunity to work with: Blake Stockman, a former colleague of mine from Uber. Blake built a strong reputation in the recruiting world, working at tech giants like Google, Meta, and Uber. He also spent time with Y Combinator and founded his agency, where he helped both large tech companies and early-stage startups find and secure top talent. A few months ago, Blake did a career pivot: he is now studying to become a lawyer. I pounced on this perfect opportunity to have him share all that he’s seen behind-the-scenes in tech recruitment: sharing his observations unfiltered. In our conversation, Blake shares recruitment insights from his time at Facebook, Google, and Uber and his experience running his own tech recruitment agency. We discuss topics such as: • A step-by-step breakdown of hiring processes at Big Tech and startups• How to get the most out of your tech recruiter, as a candidate• Best practices for hiring managers to work with their recruiter• Why you shouldn’t disclose salary expectations upfront, plus tips for negotiating• Where to find the best startup opportunities and how to evaluate them—including understanding startup compensation• And much more! — Timestamps (00:00) Intro (01:40) Tips for working with recruiters (06:11) Why hiring managers should have more conversations with recruiters (09:48) A behind-the-scenes look at the hiring process at big tech companies  (13:38) How hiring worked at Uber when Gergely and Blake were there (16:46) An explanation of calibration in the recruitment process (18:11) A case for partnering with recruitment  (20:49) The different approaches to recruitment Blake experienced at different organizations (25:30) How hiring decisions are made  (31:34) The differences between hiring at startups vs. large, established companies (33:21) Reasons desperate decisions are made and problems that may arise (36:30) The problem of hiring solely to fill a seat (38:55) The process of the closing call (40:24) The importance of understanding equity  (43:27) Tips for negotiating  (48:38) How to find the best startup opportunities, and how to evaluate if it’s a good fit (53:58) What to include on your LinkedIn profile (55:48) A story from Uber and why you should remember to thank your recruiter (1:00:09) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • How GenAI is reshaping tech hiring https://newsletter.pragmaticengineer.com/p/how-genai-changes-tech-hiring • Hiring software engineers https://newsletter.pragmaticengineer.com/p/hiring-software-engineers  • Hiring an Engineering Manager https://newsletter.pragmaticengineer.com/p/hiring-engineering-managers • Hiring Junior Software Engineers https://newsletter.pragmaticengineer.com/p/hiring-junior-engineers — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Dan Crosby, CEO and Founder of Legend Energy Advisors, joins Kirk once again to discuss the challenges at the intersection of energy demands and the data center industry, the rise of cloud computing and AI, and the importance of national security in energy independence.

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