Avsnitt
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NVIDIA calls it the RTX Spark. Some call it a portable AI supercomputer. We call it a perfectly reasonable excuse to spend an hour wandering through completely unrelated topics. In this episode, we take a look at one of the most powerful AI-focused laptops currently available, discuss what it can actually do, and ask the important question: does anyone really need this much computing power on their desk? Along the way, we dive into programming experiments, development workflows, AI tooling, hardware realities, and several detours that absolutely nobody planned for. The result is a classic technology podcast experience: a cutting-edge piece of hardware, a handful of code, a collection of opinions, and a conversation that somehow ends up everywhere except where it started. Fast hardware, questionable focus, and plenty of geeky discussion guaranteed.
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Today’s episode is dedicated to one of the greatest technological achievements of the modern era: running an AI model on a CPU because GPUs now cost approximately the same as a midsize apartment in Zagreb. We discuss the breathtaking experience of waiting 14 minutes for a response that confidently explains facts that never existed, cites imaginary research papers, and occasionally forgets what year it is. Naturally, this leads us into the philosophical debate of whether an AI that takes half an hour to answer is actually “thinking” or just emotionally processing its own poor life choices. From thermal throttling and swap-file abuse to hallucinations so convincing they deserve political office, this episode is a beautiful monument to patience, bad decisions, and the unstoppable human urge to run enterprise AI on hardware rescued from a student lab.
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Saknas det avsnitt?
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Welcome to The datacenter will be ready right after the roundabout, the only podcast brave enough to ask the important questions: can a hyperscale datacenter survive Croatian paperwork, three ministries, two environmental studies, and a mayor who still thinks “cloud” means rain? In this episode, we dive into the glorious collision of AI ambitions, megawatt fantasies, land permits, power grid realities, and the sacred regional tradition of discussing infrastructure projects for roughly twelve years before pouring a single cubic meter of concrete. We talk cooling, fiber, geopolitics, NIMBYism, diesel generators the size of apartment buildings, and the magical belief that “digital transformation” somehow works without electricity. The future is here — just as soon as somebody finishes the access road.
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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.
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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.
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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.
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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.
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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.
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In this episode, we dive headfirst into the beautifully chaotic world of modern tech, where nothing quite works the way it should—and somehow, that’s the theme. We start with Windows 11 and its ongoing talent for turning simple multimedia tasks into unsolved mysteries, then spiral into the usual driver-related frustrations that every IT professional knows all too well. Along the way, we touch on the ever-present buzz around AI—what’s real, what’s hype, and what actually works in practice. As if that wasn’t enough, we branch out into the growing influence of ARM-based chips and what they mean for the future of computing. It’s a classic mix of rants, insights, and “how is this still broken?” moments—unfiltered, slightly sarcastic, and very relatable to anyone living in today’s tech ecosystem.
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AI just made a quiet but massive shift. One of the most impressive generative AI tools ever released is being shut down as it rethinks its approach to commerce. At the same time, enterprises are doubling down on sovereign AI, agentic systems, and context-aware intelligence. This isn’t random—it’s a reset. The industry is moving away from flashy, expensive demos toward systems that are controllable, understandable, and economically viable. In this episode, we explore the connections between these shifts and their true implications: AI is maturing, and the future belongs to systems that not only impress but also effectively manage the world.
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In today’s tech landscape, the middle is disappearing. Products are either premium, high-performance, and expensive—or free, open, and “good enough.” From hardware to software, customers are no longer interested in compromise. This episode explores the rise of the barbell economy and why “average” has become obsolete. We dive into how companies that once dominated the middle—like Microsoft—are struggling to redefine their position in a world split between extremes. Should we prioritize top-tier innovation or prioritize scale and accessibility? What happens to vendors stuck in between? And more importantly—where should you position yourself in this new reality? If you’re building products, platforms, or strategies, this shift is something you can’t afford to ignore.
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AI has officially escaped the lab—and now it’s everywhere. But instead of repeating the usual stories about chatbots and image generators, we decided to run our own experiments. What happens when you throw AI at real problems, weird ideas, or everyday tasks nobody thought about automating before? In this episode, we walk through a collection of unusual, sometimes surprising, and occasionally slightly ridiculous AI use cases we recently tested ourselves. Some worked brilliantly. Some worked… strangely. And a few made us question whether we should really be giving these tools so much power. From productivity hacks to unexpected creative tricks, this episode is basically our AI playground report: what we tried, what broke, what impressed us, and what might actually become useful sooner than anyone expects. Buckle up—this one gets interesting.
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In a world preoccupied with productivity, optimization, and efficiency, hobbies often seem like a luxury or even a waste of time. But what if that’s exactly the point? In this episode, we talk about the strange, wonderful importance of doing things that don’t scale, don’t pay, and don’t necessarily make sense. Hobbies, whether they involve building model airplanes, learning guitar riffs from the 80s, restoring old computers, or designing something completely weird just for fun, provide our brains with a creative space. They create space for curiosity, experimentation, and creativity without deadlines or KPIs (key performance indicators). Ironically, the things we do purely for fun often teach us the most. So maybe productivity isn’t everything. Maybe the real trick is simple: stop optimizing for a moment… and go build something weird.
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Artificial intelligence is no longer a polished lab experiment — it’s a frontier town. In this episode, we dive into OpenClaw and what it represents in today’s rapidly shifting AI landscape: open models, decentralized power, GPU-driven innovation, and a race where regulation struggles to keep up. From open-source disruption to model autonomy, from enterprise control to digital anarchy, we explore whether this is the beginning of true AI democratization — or the calm before algorithmic chaos. Saddle up. The frontier is computational.
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We’ve all seen it. The legendary documentation. The sacred diagrams. The “fully updated” runbooks. They absolutely exist — until the moment you actually need them. Then suddenly they’re archived, outdated, in someone’s inbox from 2017, or living exclusively inside the brain of the one engineer currently on vacation. In this episode, we open the box and observe the quantum state of IT documentation: simultaneously complete and nonexistent. From ghostly Visio files to tribal knowledge passed down like ancient folklore, we explore why the most critical infrastructure artifact is also the most elusive creature in the data center ecosystem.
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For decades, robots.txt quietly told search engines where they were welcome and where they weren’t. Then AI showed up, read everything anyway, and called it “training.” Enter ai.txt — the hypothetical line in the sand where a website politely, clearly, and possibly angrily says: no scraping, no learning, no digital photocopying of my soul. In this episode, we explore whether consent still matters on the modern web, how AI crawlers differ from classic search bots, and whether ai.txt would be a genuine technical safeguard, a legal signal, or just a beautifully naive sign taped to the internet’s fridge saying “do not touch.” Spoiler: the file is tiny. The implications are not.
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Once upon a time, audio quality mattered. We argued about sound cards, speaker placement, bitrates, and whether MP3s were ruining music forever. Fast forward to today: laptops whisper, Bluetooth drops packets, compression is everywhere—and nobody seems to care. Or do they? In this episode, we explore how “good enough” became the global audio standard, why convenience beat fidelity, and how computers quietly shifted from Hi-Fi machines to voice-first productivity tools. This isn’t a nostalgic rant—it’s a reality check on how our ears, habits, and expectations have changed.
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Once upon a time, privacy meant closing a door, lowering your voice, or simply being left alone. Today, it means scrolling through settings, declining cookies for the fifth time, and hoping nobody is listening — while fully assuming someone is. We still talk about privacy as if it’s alive and well, protected by checkboxes, policies, and reassuring icons, even as our phones, homes, cars, and workplaces quietly log everything we do. This episode isn’t about panic or paranoia — it’s about honesty. Privacy didn’t suddenly die; it slowly dissolved into convenience, comfort, and “just one more app.” The real question isn’t whether privacy is gone, but why we keep pretending it isn’t — and who benefits from that fiction.
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There was a time when a photo or a video meant something simple: this actually happened. Today, that assumption is quietly falling apart. We’re entering an era where reality can be generated, faces can be borrowed, voices can be cloned, and events can be convincingly fabricated in minutes. Not as science fiction, not as satire—but as everyday tooling. This episode isn’t about panic or moral grandstanding. It’s about what happens when trust becomes optional. When proof becomes negotiable. When institutions, media, courts, and even personal relationships must operate under the assumption that what they see might not be real—and that real evidence might be dismissed as fake. Synthetic reality doesn’t just blur lines between truth and fiction; it shifts the burden of proof onto everyone, all the time. And the consequences of that shift are far more serious than most people realize.
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Working from home was supposed to give us freedom—flexibility, focus, maybe even lunch that doesn’t come from a vending machine. Instead, many of us unlocked a new achievement: being in three places at once and still disappointing everyone. One meeting overlaps another, a “quick call” eats an hour, and suddenly you’re nodding thoughtfully on mute while answering emails, Slack messages, and existential questions about time itself. Online availability made us reachable, but scheduling turned us into calendar acrobats juggling overlapping priorities, time zones, and notifications that never sleep. This episode is a short, therapeutic rant about how remote work didn’t remove chaos—it just moved it into your calendar.
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