AI Titans Unleashed: New LLMs, Karpathy’s Insights, and Numeric Experiments

Welcome back to Techsambad! In this latest edition of the podcast, I’m diving headfirst into some of the most exciting developments in the AI world. From the release of three powerhouse large language models (LLMs) to a deep dive into Andrej Karpathy’s latest video and some hands-on experiments of my own, this episode is packed with insights for anyone curious about where artificial intelligence is headed. Let’s break it down!

The Big Three: xAI, Anthropic, and OpenAI Drop New Models

The AI landscape just got a major shake-up with xAI, Anthropic, and OpenAI unveiling their latest LLMs. These releases feel like a triple-threat showdown, each model promising to push the boundaries of what AI can do. In the podcast, I share my initial impressions—xAI’s offering seems laser-focused on accelerating discovery, Anthropic’s model brings a fresh take on interpretability, and OpenAI, well, they’re doubling down on raw power and versatility. It’s too early for a full verdict, but these models are already sparking debates about performance, ethics, and real-world impact. What’s your take on this AI arms race? I’d love to hear your thoughts as these roll out!

Andrej Karpathy’s Latest: A Masterclass in LLM Mechanics

Next up, I couldn’t resist digging into Andrej Karpathy’s new YouTube video, “Deep Dive into LLMs like ChatGPT” (watch it here). If you’re not familiar with Karpathy, he’s a rockstar in the AI community, and this video is a goldmine. He peels back the layers of how LLMs work—think tokenization, attention mechanisms, and the sheer scale of training data—while keeping it engaging and digestible. What struck me most was his take on reasoning: LLMs aren’t just parroting text; they’re starting to mimic human-like problem-solving (sort of). I break it all down in the podcast, highlighting key moments and why this matters for the future of AI. If you’re an AI nerd like me, this is a must-watch.

Putting LLMs to the Test: Numeric Calculations Experiment

Theory’s great, but I wanted to get my hands dirty. So, I ran some experiments testing these LLMs on large numeric calculations—think big multiplication, exponentiation, and even some quirky factorial challenges. The results? Fascinating and a little surprising! Some models handled millions of digits like champs, while others stumbled on precision or outright refused to play ball. I dive into the nitty-gritty in the episode—how they performed, where they struggled, and what it says about their design. Spoiler: LLMs aren’t calculators, but they’re getting eerily close. Tune in for the full scoop!

Why This Matters

These releases, Karpathy’s insights, and my experiments all point to one thing: AI is evolving fast. Whether you’re a developer, a researcher, or just someone who loves tech, this is a thrilling time to watch the space. The new LLMs could redefine industries, Karpathy’s breakdowns help us understand the “how,” and hands-on tests show us the limits (and potential) of today’s tech.

Check out the full episode of Techsambad on Substack, Spotify, or your favorite platform. Let me know what you think of these models or if you’ve tried any AI experiments of your own—I’m all ears!

Happy listening,