LLMs - From poker nights to mission-critical systems | Thy Virtuoso | Episode 1 скачать в хорошем качестве

LLMs - From poker nights to mission-critical systems | Thy Virtuoso | Episode 1

In this deep dive, we move past the novelty of chat interfaces to explore the gritty reality of building mission-critical systems with LLMs. Using real-world failures…from "poker night" hallucinations to mismanaged data, we break down why LLMs are inherently probabilistic "liars" and how engineers can architect around their sycophantic nature. We shift the focus from simple prompt engineering to the "Fringe Stack," where the real enterprise value lies: 1. Building validation layers. 2. Leveraging Graph Attention Networks. 3. Using Mixture of Experts (MoE) to solve complex, low-data problems. Whether you're navigating the tactical utility of data cleaning or the strategic complexity of multimodal spatial analysis, this video outlines a roadmap for keeping AI on a leash while building the deterministic anchors necessary for high-stakes deployment. Timestamps: 00:00:00 - Podcast recap 00:01:30 - Meet the panel 00:01:43 - Magic & the mess with AI 00:02:15 - The "Yes-Man" problem: Taming LLM sycophancy 00:03:18 - Prathamesh's interview assignment 00:04:20 - Unexpected ChatGPT experiments 00:05:00 - Shifting from chatbot to engine 00:06:30 - The tactical layer: Automating low-level tasks 00:08:04 - The strategic layer: AI as a high-level sounding board 00:09:22 - The LLM as an intellectual companion 00:11:38 - Sumanth’s playbook: The top two usage frameworks 00:12:04 - Power of native multimodality 00:12:55 - Real-world perception: Propheous & spatial use cases 00:14:45 - Future proofing: LLMs as a fundamental capability 00:16:33 - The Pivot for undergraduates: Core stack vs. "Fringe stack" 00:18:00 - Software vs. Domain expertise 00:19:09 - Why young engineers must stay critical 00:21:20 - How much should we trust AI? 00:23:58 - User interface hacks for efficiency 00:24:44 - Architectural edge: Leveraging attention networks 00:26:08 - The research partner: Deep diving with AI 00:26:43 - Search vs. Query 00:27:14 - Verifying through hard sources 00:27:47 - Prompting mastery 00:28:40 - Optimizing ChatGPT & Perplexity 00:29:01 - Knowing when LLMs fail 00:29:30 - Where the next big opportunities lie 00:30:30 - The trust deficit, validation & verification systems 00:34:40 - The new frontier: Why AI R&D is the best opportunity 00:35:27 - Synthesis: Key learnings and executive summary 00:36:53 - Citation models 00:39:00 - Identifying the two main LLM flaws 00:39:38 - Managing shortcomings 00:39:41 - Verification hacks 00:41:12 - Urban intelligence: Tracking development at Propheous 00:42:34 - Anchoring models in reality 00:44:32 - The enterprise leap: Selling AGI to organizations 00:46:00 - Will AGI displace the workforce? 00:46:39 - The data goldmine: Rethinking enterprise strategy 00:48:52 - Empowering teams via AI 00:49:30 - How enterprises view AI adoption 00:50:31 - Architecture shift: Transformers across disciplines 00:53:21 - Beyond text: Graph models and attention networks 00:54:02 - The next horizon: Emerging architectures and model arrays 00:55:08 - Small Language Models & cognitive science 00:56:26 - Deploying an array of LLMs 00:57:50 - Human brain's sophistication 00:59:12 - Self-evaluation: Auditing your own tech usage 00:59:44 - Gut-based decisions and AI gaps 01:01:52 - Fun bit - Matching LLMs with famous personalities