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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.

Like, Subscribe, and Follow the Tech Overflow Podcast by visiting this link: https://linktr.ee/Techoverflowpodcast