That IT show

Somewhere along the way, “learning virtualization” turned into running a miniature Fortune 500 datacenter next to the washing machine. In this episode, we dive head-first into the glorious madness of over-engineered home labs: redundant power supplies for one user, Kubernetes clusters hosting absolutely nothing important, backup strategies more detailed than our retirement plans, and the eternal justification that “it’s for learning.” We talk about the slippery slope from a single Raspberry Pi to racks full of servers screaming through the night, why every homelabber eventually discovers VLANs at 2 AM, and how disaster recovery suddenly becomes deeply personal when Plex goes offline. If you’ve ever convinced yourself that a 100-gigabit upgrade was “necessary,” this episode may feel uncomfortably familiar.

At some point, podcasts stopped being special and became background radiation. Every celebrity has one, every startup founder has three, and somehow every conversation now needs microphones, RGB lighting, and a “don’t forget to like and subscribe.” So naturally, we decided to contribute to the problem. In this episode, we spiral through podcast fatigue, algorithm-driven content sludge, endless “thought leaders,” and the strange realization that we’ve started losing interest in the very thing we once loved listening to. This is not an expert discussion. It’s more like a support group for people emotionally exhausted by content.

Welcome to The One Where Linux and AI Were Ready, but Jasmin Was Not — a title that, honestly, wrote itself. The plan was simple: brand-new shiny Linux machine, proper setup, and finally an episode recorded exactly as promised. Linux was ready. AI was ready. The hardware was ready. Confidence was also very ready. And yet, somehow, the actual recording part remained a distant dream. So naturally, we turned that small technical betrayal into comedy and used it as the perfect launch point for a broader conversation about AI, hype, tools, promises, and the timeless truth that even the smartest technology in the world still depends on humans not forgetting the one thing they were supposed to do.

Welcome to Hallucinations, Hype, and Other AI Headaches — an episode about what starts to happen when artificial intelligence stops being just a tool and starts acting like a mirror, a therapist, a hype man, and sometimes a really confident idiot. We’ll talk about why AI people get uneasy when chatbots become too agreeable, too persuasive, too human-like, or simply too embedded in everyday life. From hallucinated facts and overconfident nonsense to emotional attachment, bad advice, sleepless scrolling, and machines that validate our worst ideas, this is the strange space where innovation meets irritation. AI is brilliant, useful, fascinating — and also increasingly weird. So today, we’re unpacking the headaches, the warning signs, and the uncomfortable questions that come with letting machines talk back, flatter us, mislead us, and quietly reshape how we think, work, and relate to one another.

A simple packaging mistake exposes thousands of lines of internal AI code—and within days, it’s already being used as a malware lure. At the same time, a new model emerges that can reportedly discover zero-day vulnerabilities across operating systems and browsers, raising a serious question: are we building tools that are too powerful to release? In this episode, we break down the Claude Code leak, the controversial Mythos model, and what they reveal about the future of AI. This isn’t just about security—it’s about control, responsibility, and whether the industry is ready for what it’s creating.