Nvidia
American multinational technology company
Episodes
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#5485: Fine-Tuning Parakeet for Hebrew and Your Own JargonNVIDIA's Parakeet beats Whisper on Android — but can you teach it Hebrew, or just your own jargon? Two answers, one much happier.Main topic -
#5389: Hugging Face vs Kaggle: Where Models Actually LiveHugging Face and Kaggle aren't rivals — one is infrastructure, one is a practice field. Here's how the two platforms actually differ.Main topic -
#5765: Why You've Never Heard of Schneider ElectricIt's on your breaker panel, your UPS, and half the factories on earth — so why is Schneider Electric almost invisible? -
#5764: What Does Siemens Actually Make?Factories, buildings, trains, power grids — Siemens is everywhere and nearly invisible. A tour of the industrial giant most people can't explain. -
#5749: From Sand to Trillion-Dollar Chips: Semiconductors in 5 LevelsA bad conductor and a bad insulator — and the most important material in civilization. Semiconductors explained in five escalating levels. -
#5464: Your Keyboard's Hidden Data ProblemYour keyboard knows your email address, your phrases, your habits — and you can't take any of it with you. -
#5456: Parakeet vs Whisper: Picking a Phone ASR ModelWhy Whisper loses on Android, why Parakeet v2 beat v3, and how to benchmark speech-to-text without any tooling. -
#5447: The Models That Never Talk BackSome models read text, score it, and return a number. No chat, no reasoning, just decisions — and they're running under every router you use. -
#5422: Switching Android Keyboards Without the Tap DanceOne listener wants a one-tap jump between his Parakeet voice keyboard and his typing keyboard. Android says no — unless you know the trick. -
#5396: Teaching a Small Model to Stop Spelling Out NumbersYour ASR pipeline is fine until someone dictates "three point two" and gets "three point two" spelled out. Here's how inverse text normalization ac... -
#5301: Why Talking Robots Are Really a CommitteeThat humanoid chatting while it moves? It's not one brain — it's a stack of separate models glued together. -
#5183: Where AI Actually Sits on the Hype CycleAI has been doing real work for decades. So why do vendors still shout it from the rooftops? -
#5830: Serving Your Own Fine-Tuned Model in the CloudYou fine-tuned an open-weight model. Now how does anyone actually talk to it? Dedicated GPUs vs serverless inference, and the math that decides it. -
#5435: When Your TTS Model Eats the NumbersNumbers, dates, and acronyms break text-to-speech in specific, documented ways. Here's where normalization lives — and why it depends on your model. -
#5430: Two Boxes: ASR and the Text Fixer Behind ItPunctuation, casing, ITN, disfluency — the four-job layer between raw ASR output and text you can actually read. -
#5419: The Attention Budget in Your PocketWhy phone dictation runs out of room, and how bounded attention windows buy you punctuation without blowing your memory budget. -
#5390: Chaining Small Models for Dictation CleanupDaniel's Android dictation fork won't render "three point five" as a decimal. How many models does cleanup actually need? -
#5117: How AI Training Data Gets Filtered (and Exploited)Six stages of content filtering stand between raw web crawls and your AI model — here's where poisoning attacks slip through.