Steal breakthrough ideas in tech, business & performance from world-class experts <br/><br/><a href="https://www.hockeystick.show?utm_medium=podcast">www.hockeystick.show</a>
HockeyStick Show
Claim This Podcastby Miko Pawlikowski
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58 episodes
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Podcast Overview
Steal breakthrough ideas in tech, business & performance from world-class experts <br/><br/><a href="https://www.hockeystick.show?utm_medium=podcast">www.hockeystick.show</a>
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Publishing Since
3/16/2024
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Recent Episodes
June 20, 2026
Just Use Postgres?, with Denis Magda - HockeyStick #55
<p>Hello everyone!</p><p>I’m Miko Pawlikowski, and in this #55 episode of The Hockey Stick Show, I sat down with Denis Magda to talk about one of the most widely used databases in the world: Postgres.</p><p>Denis recently published a book called Just Use Postgres through Manning, and our conversation explored why this decades-old technology continues to power modern applications, often replacing entire categories of specialized databases.</p><p>Rediscovering Postgres</p><p>Many engineers have a complicated relationship with Postgres.</p><p>For some, it’s the dependable database that’s always there. For others, it can feel like an aging piece of infrastructure overshadowed by newer and more specialized alternatives.</p><p>Denis first started using Postgres in 2009 while working on social networking applications. Over the years, it became his preferred database because of its reliability, maturity, and flexibility.</p><p>His perspective deepened when he joined Yugabyte, a company building distributed Postgres solutions. There, he began exploring capabilities that many developers overlook, including JSON support, full-text search, and vector similarity search.</p><p>Those discoveries ultimately inspired him to write Just Use Postgres.</p><p>Beyond a Relational Database</p><p>One of the central themes of our discussion was the difference between the “best” tool and the “right” tool.</p><p>Postgres may not outperform every specialized database in its respective niche. Dedicated document databases, search engines, or vector databases often offer deeper functionality for specific use cases.</p><p>That doesn’t mean you should automatically add them to your stack.</p><p>Denis advocates starting with a proof of concept to determine whether Postgres can meet your requirements before introducing additional infrastructure. In many cases, it can.</p><p>This philosophy isn’t about replacing every database with Postgres. It’s about reducing unnecessary complexity and making informed architectural decisions.</p><p>The Swiss Army Knife of Databases</p><p>Throughout our conversation, Postgres increasingly resembled a Swiss Army knife.</p><p>Its extensible architecture allows developers to add capabilities through a rich ecosystem of extensions. Whether you need geospatial functionality, scheduling capabilities, advanced indexing, or AI-related features, chances are there’s already an extension available.</p><p>This adaptability has helped Postgres remain relevant through multiple generations of technology trends.</p><p>Rather than trying to reinvent itself every few years, it continues to evolve while maintaining the stability that production systems depend on.</p><p>A Community-Driven Success Story</p><p>Another fascinating topic was the governance model behind Postgres.</p><p>Unlike many popular technologies, Postgres isn’t controlled by a single vendor. Instead, it thrives through a community-driven approach that prioritizes collaboration and long-term sustainability.</p><p>Denis compared its ecosystem to Linux. Multiple companies contribute to the project, but no single organization dictates its direction.</p><p>This balance has allowed Postgres to maintain exceptional quality, resilience, and independence while continuing to innovate.</p><p>According to Denis, that community stewardship is one of the key reasons why Postgres has remained relevant for so long.</p><p>Message Queues, Job Scheduling, and More</p><p>One of the more surprising parts of our discussion was how far Postgres can stretch beyond traditional database workloads.</p><p>Denis explained that Postgres can serve as a job queue or lightweight messaging system for many applications.</p><p>It’s not intended to replace platforms like Kafka for large-scale event streaming, but for simpler workloads, it can often handle job scheduling and message processing effectively.</p><p>Features such as partitioning, indexing, and extensibility make these use cases practical without introducing additional operational overhead.</p><p>Why Simplicity Matters</p><p>A recurring theme throughout the episode was simplicity.</p><p>Every new component added to a system introduces operational costs, maintenance requirements, and potential points of failure.</p><p>Denis argues that engineers should challenge the assumption that every problem requires a new database or service.</p><p>Sometimes the simplest solution is already running in production.</p><p>Sometimes the answer is simply: “Just use Postgres.”</p><p>Final Thoughts</p><p>Denis’s book offers a fresh perspective on a technology many developers think they already know.</p><p>Whether you’re a long-time Postgres user or simply curious about what modern Postgres is capable of, Just Use Postgres is packed with practical insights and real-world examples.</p><p>You can find the book through Manning, Amazon, and other major booksellers.</p><p>To follow Denis and his work, connect with him on LinkedIn and X, where he regularly shares thoughts on databases, distributed systems, and software architecture.</p><p>It was a pleasure having Denis on the show, and I hope you enjoy the episode as much as I enjoyed the conversation.</p><p>Thanks for reading, and see you in the next one.</p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://www.hockeystick.show?utm_medium=podcast&utm_campaign=CTA_1">www.hockeystick.show</a>
April 11, 2026
The Human Blind Spot in Cybersecurity, with Robert Siciliano - HockeyStick #54
<p><strong>Welcome to Episode 54 of The HockeyStick Show</strong></p><p>I’m Miko Pawlikowski, and this week we explored the evolving world of cybersecurity with Robert Siciliano.</p><p>Robert, CEO of Protect Now LLC and creator of the Strategic Human Firewall, joined us to unpack how organizations can adapt to increasingly sophisticated threats. The conversation moved beyond tools and tactics into something deeper: how people, not just systems, define the strength of modern security.</p><p>Understanding the Human Firewall</p><p>We started with Robert’s core idea: the “human firewall.”</p><p>At its essence, it’s about transforming employees from passive liabilities into active defenders. Traditional firewalls filter traffic. Human firewalls filter intent.</p><p>Instead of relying solely on technical controls, this approach builds awareness, judgment, and instinct across the organization. Employees aren’t just following rules. They’re recognizing risk in real time.</p><p>Robert drew a sharp contrast with standard security training. Most programs focus on compliance and minimal engagement. His model pushes toward something more durable: personal ownership.</p><p>Security Awareness vs. Security Appreciation</p><p>One of the most important distinctions Robert made was between awareness and appreciation.</p><p>Awareness is surface-level. It means knowing the rules.</p><p>Appreciation goes further. It means understanding why those rules matter and acting accordingly.</p><p>He illustrated this with what he calls the “kitchen table effect.” When employees internalize security lessons deeply enough, they bring them home. They talk about them with family. They apply them in everyday life.</p><p>That’s when behavior actually changes.</p><p>Security stops being a corporate requirement and becomes a personal value.</p><p>AI: The New Frontier</p><p>We also spent time on AI and its impact on cybersecurity.</p><p>Robert was clear: AI raises the stakes. Deepfakes, synthetic voices, and hyper-personalized phishing attacks make deception more convincing than ever.</p><p>Old mass phishing campaigns are fading. What’s replacing them is precision targeting at scale.</p><p>But there’s a flip side.</p><p>This shift creates a moment to re-engage people. When threats feel more real and more personal, training can become more relevant. More urgent. More effective.</p><p>The Personal Touch</p><p>As the conversation wrapped, Robert emphasized a simple principle:</p><p>Security works best when it feels personal.</p><p>Titles don’t matter here. Whether you’re a CTO, CISO, or team lead, your role is to make security relatable.</p><p>That might mean sharing stories. Running discussions during all-hands meetings. Talking about real-world examples, including how these threats show up at home, not just at work.</p><p>When people see themselves in the problem, they start to care about the solution.</p><p>Taking Action</p><p>For organizations looking to improve, Robert suggested starting with a basic question:</p><p>How does security currently show up in your company?</p><p>If it feels like a checklist, that’s the problem.</p><p>Reframe it as something empowering. Something human.</p><p>Build training that invites participation. Encourage questions. Create space for real conversations instead of one-way instruction.</p><p>The goal is to make security part of the culture, not just a requirement.</p><p>Final Thoughts</p><p>Robert Siciliano’s perspective is straightforward but often overlooked.</p><p>Technology matters. But mindset matters more.</p><p>If people care, they pay attention. If they pay attention, they catch things machines miss.</p><p>Security isn’t just a systems problem.</p><p>It’s a people problem.</p><p>And that’s exactly where the opportunity is.</p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://www.hockeystick.show?utm_medium=podcast&utm_campaign=CTA_1">www.hockeystick.show</a>
February 21, 2026
Inside OpenAI: the Future of Deep Learning, with Richard Heimann - HockeyStick #53
<p>Welcome to Episode 53 of The HockeyStick Show. I’m Miko Pawlikowski, and this week I sat down with Richard Heimann, Director of AI for the State of South Carolina and author of “Sutskever’s List”, to talk about the papers that built modern AI, the man behind OpenAI’s biggest breakthroughs, and what happens when living doubts become explosive decisions.</p><p>Richard walked me through Ilya Sutskever’s legendary reading list: 27 papers that supposedly explain 90% of what’s happening in artificial intelligence, and why understanding this curated canon matters more than drowning in the weekly flood of new research. The conversation moved fluidly between deep learning history, the Sam Altman firing saga, bubble economics, and the challenge of separating genuine progress from AGI fever dreams.</p><p><strong>The Reading List That Became a Book</strong></p><p>We started by exploring how a simple recommendation from Ilya to John Carmack turned into a full book project. When Ilya shared his reading list in 2021 or 2022, he made a promise: read these papers and you’ll understand 90% of what’s going on in AI.</p><p>Manning Publications initially wanted an anthology: 27 chapters analyzing each paper in isolation. Richard pushed back. The papers weren’t just standalone artifacts; they built on each other and told a larger human story. Ilya’s story. The publisher agreed, and Richard spent the last year weaving the technical breakthroughs into a narrative that makes sense for people who aren’t writing these papers themselves.</p><p>The book is done. The final chapters just went up on Manning’s early access program. Print release is scheduled for May 2025.</p><p><strong>Who Is Ilya Sutskever and Why Should We Care?</strong></p><p>For those who only know Ilya from the Sam Altman firing drama, Richard provided crucial context. This is the person responsible for AlexNet in 2012: the moment that launched the modern deep learning era. He’s behind Word2Vec, sequence-to-sequence models, and the scaling of transformers at OpenAI. GPT-1, 2, 3, and beyond.</p><p>But beyond the technical contributions, Ilya has this mystique. He doesn’t say much. When he does, it’s high signal. And his work has consistently centered on safety concerns, which makes him both a technical innovator and someone genuinely worried about the implications.</p><p>The reading list reflects his mental model. It gives insight into what he sees, what he values, and why he makes the decisions he makes.</p><p><strong>The Sam Altman Firing: Living Doubts Gone Wrong</strong></p><p>We spent significant time unpacking the OpenAI board saga. Richard’s take was fascinating: he traced it back to GPT-2 in 2019, when OpenAI deemed the model “too dangerous to release” and staged its rollout over nine months.</p><p>At the time, researchers were skeptical. It looked like hype-building. But Richard sees it differently now: it was a living doubt. Ilya and OpenAI acted on their safety concerns in a transparent, reversible way. They could always say “we were wrong” and release the full model, which they eventually did.</p><p>The Sam Altman firing was different. It was explosive, irreversible, and impossible to unwind once initiated. The lesson from a safety perspective: whatever your doubts are, structure them so you can reverse course if you’re wrong.</p><p><strong>Bubble Economics and the Free Lunch Era</strong></p><p>I asked the question everyone wants answered: are we in an AI bubble?</p><p>Richard’s response was nuanced. Yes, it’s bubbly. But bubbles aren’t inherently bad. Nothing important happens without bubbles. You don’t get this kind of capital, talent, and momentum from purely rational actors making measured bets.</p><p>The key difference from 2008: there’s real underlying technology here. It’s more like the dot-com bubble: bad ideas will get flushed out, valuations will correct, but the fundamental shift is genuine.</p><p>What’s remarkable isn’t the diminishing returns everyone’s complaining about. It’s that scaling worked at all. For 50-60 years, AI progress required genuine innovation: new architectures, new training tricks. For the last five years, we just made models bigger and threw more data at them. That free lunch was unprecedented.</p><p>Now the free lunch is ending. Ilya himself recently said the era of scaling is over. We’re going to need good ideas again.</p><p><strong>AGI: Paper Hopes vs. Living Technology</strong></p><p>Richard was refreshingly direct about AGI hype. He doesn’t find the concept appealing. It’s a paper hope: something people talk about but don’t actually build toward in meaningful ways.</p><p>The substrate we’re working with isn’t going to produce human-like intelligence. And we don’t need it to. The technology is already powerful and will continue improving linearly. But the exponential curves and S-curves are done. We’re hitting asymptotes.</p><p>The implication: a lot of the AI safety concerns about alignment and existential risk become less urgent. He doesn’t see an existential threat from his computer.</p><p><strong>What’s Underrated and Overrated</strong></p><p>I asked Richard what people are sleeping on and what’s empty hype.</p><p>Overrated: AGI and the entire AI safety research agenda focused on existential risk.</p><p>Underrated: The technology itself, at least among skeptics. Too many people dismiss these models as “stochastic parrots” or “just databases” without understanding what they actually are. The technology will be pervasive in five to ten years, and the skeptics are needlessly rounding down.</p><p><strong>Working in Government AI</strong></p><p>We also covered Richard’s day job: Director of AI for South Carolina. He evaluates use cases from 80+ state agencies, all interested in adopting AI. Some have clear ideas, others need help defining their approach.</p><p>About 80% is advisory: looking at use cases from technical, governance, privacy, and security perspectives. The remaining 20% is an informal accelerator developing strategic use cases in-house.</p><p>The scale is what attracts him. Even in a small state of 5 million people, the potential impact is enormous.</p><p>At its core, this episode was about understanding foundations in a field that rewards chasing novelty. How to build mental models that persist beyond the next model release. How to act on doubts without making irreversible mistakes. And what it takes to write a book that captures not just the papers, but the worldview behind them.</p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://www.hockeystick.show?utm_medium=podcast&utm_campaign=CTA_1">www.hockeystick.show</a>
58 total episodes available
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