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A fake contractor calls the help desk, a password gets reset, and suddenly a national retailer has hackers inside. We open the door on the human side of hacking—how believable stories and helpful habits become the first domino—then trace the technical steps that turn a small foothold into a system‑wide crisis.

We walk through the anatomy of the Marks & Spencer breach: social engineering as the entry point, slow‑burn privilege escalation, and the moment attackers reached the Active Directory—the store of who can do what. From there, it’s a short hop to ransomware detonation and double extortion, where every machine is unusable and stolen customer data adds pressure to pay. Along the way, we translate hashing, brute force, and admin access into plain English, and we talk candidly about what detection looks like when it actually works: least privilege that’s enforced, behavioural alerts that catch odd access patterns, and teams empowered to say no.

The hardest lesson lands in recovery. Backups that live on the same network get encrypted or deleted; backups that are never rehearsed don’t restore on time. We break down air‑gapped, immutable backups, how to test restores, and why a clean rebuild is sometimes the only safe path. We also connect this case to higher‑stakes incidents at pipelines and hospitals, showing why attackers chase critical bottlenecks and how zero‑trust identity, MFA, network segmentation, and vendor risk controls blunt that leverage. It’s a story about culture as much as code: small process choices—like verifying contractors—change outcomes.

If this breakdown sharpened your thinking, follow the show, leave a quick review, and share it with a teammate who owns identity, help desk, or backups. Your support gets us to series two—and might just get Hannah to Melbourne.

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Finally, a podcast that explains how AI, LLMs, and ChatGPT work without any hype, fluff, or hyperbole. This episode is aimed at smart people who aren’t in tech and just want to be able to understand the basics. Join host Hannah Clayton-Langton as she discusses the topic with former Google VP and OG AI expert, Hugh Williams.

We start by separating AI, machine learning, and LLMs, then explain why generative systems are not search. Instead of retrieving pages, an LLM synthesises new text using patterns learned from trillions of tokens. That leap was unlocked by transformers, the architecture that parallelises processing and models relationships between words through attention. Add weeks of GPU-heavy training in massive data centres and you get astonishing next-word prediction with long-range context.

Then comes the human layer. We talk through reinforcement from human feedback that nudges models toward helpful, safe behaviour, and the safety heuristics that block harmful queries or intercept trivial ones. We also get candid about limits: hallucinations that produce confident nonsense, bias from data and raters, weak arithmetic unless the system calls an external tool, and uneven image generation that’s improving fast. Along the way we share practical tips: how to compare outputs across models, when to fact-check with a second system, and why grounding responses in reliable sources matters.

If you’ve heard about trillion-token training runs, NVIDIA GPUs, and “stochastic parrots” but want a clear, human explanation, this one’s for you. You’ll learn how LLMs actually work, why they feel so capable, and how to use them at work like a fast intern whose drafts still need your judgement. Enjoy the deep dive, and if it helps you explain AI to a friend, subscribe, leave a review, and share your favourite takeaway with us.

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What happens when a search engine is driven by a text file of hand-written rules? You get a Jaguar car ranking first for an iPod query on eBay, and you get the perfect setup for a practical tour of how AI actually creates value. We unpack the journey from brittle if-then logic to machine learning that learns relevance from real outcomes.

In this episode, we break down AI, machine learning, and large language models (LLMs) in clear terms, showing how they fit together and where they differ. We discuss in depth the first wave of AI systems that solve specific problems from detecting credit card fraud, to ranking search results, to recommending the movies we watch. These AI systems are everywhere -- most everything you use at Meta, Google, Microsoft, Amazon, and Apple is driven by machine learning and AI at its core.

This episode also discusses the fundamental truth that great AI starts with fresh, comprehensive, clean data, and a well-defined target. We explain that most companies still aren't getting this right and that no AI system will be effective if there's garbage data.

The most surprising lesson might be the most useful: sometimes the right answer is not to use AI. Hear the story of a 99%‑accurate model that surfaced a company’s fax number as customer support, and why a small human team delivered safer, cheaper, 100%‑correct results. We also explore why LLMs feel like a revolution—real breakthroughs plus a brilliant, accessible UX—and how that shift is changing how people find information.

If you care about building reliable AI products, avoiding unforced errors, and making smarter trade-offs, this conversation will sharpen your instincts. Listen, share with a colleague who loves a good data debate, and subscribe so you don’t miss part two, where we dive into deep learning, transformers, and what’s coming next.

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Tinder's #swiperight gesture changed how millions decide and revolutionised dating. Tinder didn’t just explode into the public consciousness, it was also the most successful dating product in history and one of the fastest companies to $100m in revenue.

Hannah and Hugh sit down with Tinder co‑founder Jonathan Badeen to trace the unexpected path from a flashcards epiphany to a cultural verb, and why #swiperight wasn’t meant to be the defining feature until a college student sent him an email.

Jonathan opens the hood on the craft behind the card: how a mobile‑first mindset shaped a one‑at‑a‑time interface, why he ditched Apple’s default swipe gesture recogniser, and how velocity‑aware, quadrant‑based rotation made the interaction feel alive in your hand. We get into naming drama, the fire‑themed brand, and the quiet logic of placing “yes” on the right, then follow the story through onboarding shortcuts, texting‑style messaging, and the decision to delay heavy tutorials in favour of just‑in‑time education.

The conversation goes deeper than UI. Jonathan explains the gritty systems work that kept Tinder feeling instant on fragile networks—preloading batches, caching swipes offline, filtering duplicates when servers fell out of sync—and how the team balanced freshness with efficiency. We revisit the polarising #Superlike, the design thresholds needed to resolve diagonal ambiguity, and the flourish that made a paid signal feel special without breaking the core gesture. We also talk patents, clones, and why many copies miss the subtle “feel” by locking animation to rails instead of responding to the user’s touch.

For builders and the simply curious, this is a masterclass in product thinking: constraints as creativity, minimal surfaces with maximum clarity, and the humility to let the market teach you what matters. Jonathan’s advice for founders is refreshingly human—prepare widely, meet people, say yes to opportunities, and remember that execution beats secrecy.

If this story sharpened your instincts, follow the show, share it with a friend who loves product, and leave us a review.

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Catastrophic software failures can seem like acts of chaos, but behind every major tech outage lies a story of human decisions, technical constraints, and cascading consequences. The July 2024 CrowdStrike incident—which Hannah describes as "the single biggest outage in the history of computing"—offers a perfect case study into what happens when critical systems fail.

Hannah and Hugh dive deep into how a seemingly minor error (a file with 21 fields when the software expected 20) managed to crash millions of Windows computers worldwide, grounding flights, shutting down hospitals, and causing billions in economic damage. Hugh walks us through the technical underpinnings of why this particular failure was so devastating—CrowdStrike's Falcon security software runs deeply embedded within Windows, making a simple mismatch catastrophic rather than merely inconvenient.

The conversation explores the safeguards that many companies use that could have prevented this disaster: progressive rollouts, chaos engineering (Netflix's deliberately disruptive "Chaos Monkeys"), and fuzz testing that generates random inputs to break systems before they reach production. Hugh shares war stories from his own career, including a nine-hour eBay search outage that cost millions and a Google Maps bug that inadvertently became an international incident when labels disappeared from politically sensitive regions.

What's particularly fascinating is the cultural side of managing technical risk. The most resilient organizations have moved beyond blame to create environments where finding bugs is celebrated rather than punished. Hugh and Hannah discuss how former military personnel often excel in operations roles during crises, bringing calm structure to chaotic situations, and why the best tech companies are working toward systems so resilient that engineers being woken up at night is becoming unnecessary.

Whether you're part of tech or tech-enabled company or simply curious about the infrastructure powering our lives, this episode reveals the balance between innovation speed and operational stability that every technology organisation must navigate. How do you move fast without breaking things? How do you recover when systems inevitably fail? And what separates organisations that learn from failure from those doomed to repeat it?

If you’ve enjoyed this episode, please like, subscribe, or follow Tech Overflow and share it with your friends and colleagues.

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Ever wondered what's really happening behind the scenes when you tap that app icon on your phone? From the sensors tracking your every move to the complex business decisions determining which features you get access to, the world of mobile apps is fascinating.

Hannah Clayton-Langton and Hugh Williams, former VP at Google and eBay, break down why companies invest millions in app development instead of just using mobile websites. The answer lies in the incredible capabilities of your smartphone – packed with nearly 20 different sensors that apps can access to create personalised, responsive experiences. Beyond the obvious cameras and GPS, your phone contains barometers measuring elevation changes, magnetometers functioning as compasses, and accelerometers tracking how your phone accelerates and decelerates. This sensor-rich environment enables everything from fitness tracking to navigation and even fraud detection.

The differences between Android and iOS development reveal insights into how tech companies operate. At Google, Hugh explains how Android apps would pioneer new features with iOS versions "fast following" six months later after seeing user reactions. This approach highlights the careful balancing act between innovation and stability that defines modern app development. Similarly fascinating is how different devices get different experiences – laptops for deep exploration, phones for quick "snacking" interactions, and tablets for activities requiring more screen real estate.

Hugh and Hannah also discuss the business model behind app stores, with Apple and Google taking up to 30% of all purchases and subscriptions – a "tax" that's sparked legal challenges in the EU. And if you've ever wondered how some apps keeps working when you lose your connection, Hugh reveals the sophisticated caching techniques that predict when you'll lose connectivity and download content in advance (which we'll hear more about in Episode 5!). These glimpses behind the digital curtain will forever change how you think about the apps you use every day.

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Ever wondered what makes your favorite apps work so seamlessly—or why others feel frustratingly clunky? The secret often lies in the mysterious realm of product management. Join Hannah Clayton-Langton and Hugh Williams to learn more.

Hugh Williams, former engineering vice president at Google and eBay, and a senior engineer at Microsoft, takes us behind the digital curtain to reveal how great technology products actually get built. With insider stories from his career, Hugh explains that effective product management happens at the perfect intersection of understanding customers, business objectives, and technological possibilities.

The conversation shifts from theoretical to practical as Hugh reveals how he co-invented Infinite Scroll—that ubiquitous feature that lets you scroll endlessly through content on platforms like Instagram and TikTok. What started as a simple question ("Why are there only 20 images per page?") during an analysis of user behavior led to a revolutionary change in how we interact with digital content today.

Hannah takes the conversation through case studies of both triumphs and failures—from Microsoft Word's strategic victory over WordPerfect to Google's confusingly fragmented messaging strategy— while Hugh illuminates why some products dominate while others fade into obscurity. You'll discover why technical knowledge matters for product managers, how "healthy tension" between product and engineering teams drives innovation, and why constantly monitoring competitors (Hugh admits to regularly using Apple Maps while running Google Maps) keeps products sharp and relevant.

Whether you work in tech, interact with digital products daily, or simply wonder how the apps and software you use came to be, this episode offers fascinating insights into the people and processes that shape our digital experiences.

Here's some useful links:

• PM at UC Berkeley: https://executive.berkeley.edu/programs/product-management-program
• PM at CMU: https://www.cmu.edu/tepper/programs/master-product-management/index.html
• Product Training at The Product School: https://productschool.com
• Dan Olsen's Lean Product Book: https://leanproductplaybook.com/
• Outcomes over Output book and resources: https://medium.com/@jseiden/getting-started-with-outcomes-9b136178eb07

Subscribe now and join us as we continue to demystify the technology that powers our modern lives: https://linktr.ee/Techoverflowpodcast

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Ever wondered what coding actually is but felt too intimidated to ask? You're not alone. In this beginner-friendly exploration of programming basics, we break down complex technical concepts into digestible, relatable pieces.

Our Episode 1 pilot explores the world of coding fundamentals through a metaphor: baking a cake. Just as bakers follow recipes with specific steps, measurements, and repeated actions, programmers create instructions for computers to follow. We also dive into the world of programming languages, explaining why Python is perfect for beginners while still powering advanced AI applications, how JavaScript brings websites to life, and why knowing "ancient" languages like COBOL can surprisingly lead to lucrative career opportunities today.

We also unpack the differences between coding, programming, and software engineering while addressing the burning question: will AI replace human programmers? (Spoiler: it's more like power tools for carpenters than a replacement for human creativity and problem-solving.)
Whether you're tech-curious, contemplating a career change, or simply want to understand what your software engineer friends are talking about, this episode provides a foundation for understanding the digital world around us.

If you'd like to explore a couple of links, we recommend checking out:

• Programming for Everybody (Getting Started with Python) on Coursera. A course take by over 3 million people! https://www.coursera.org/learn/python
• Hugh co-founded the Aussie charity Code For Schools that provides free and open resources to teachers who want to teach coding. Check out: https://codeforschools.com
• The story of the Voyager space probes in pictures: https://www.pbs.org/the-farthest/mission/voyager-history-photos/
• A longer feature piece on Ada Lovelace: https://writings.stephenwolfram.com/2015/12/untangling-the-tale-of-ada-lovelace/
• The Grace Hopper Conference / Celebration: https://ghc.anitab.org/

Follow us on LinkedIn at Tech Overflow Podcast or visit https://techoverflowpodcast.com for additional resources, and tune in next time as we explore the world of product management!

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