In this episode of Neural Intel, we break down Andrej Karpathy’s "2025 LLM Year in Review," exploring the massive paradigm shifts that redefined artificial intelligence over the last year. From the technical evolution of the training stack to the cultural phenomenon of "vibe coding," 2025 marked the transition from simple chatbots to "summoned ghosts" and autonomous agents,.Key topics we cover:• The Rise of RLVR: Discover why Reinforcement Learning from Verifiable Rewards (RLVR) has replaced RLHF as the de facto final stage of LLM training, enabling models to develop "reasoning" strategies by solving math and code puzzles,.• Jagged Intelligence: Karpathy argues that AI is not a "growing animal" but a "summoned ghost". We discuss why LLMs can be polymath geniuses in one moment and confused grade-schoolers the next—a phenomenon known as jagged performance.• The "Vibe Coding" Revolution: Learn how programming has shifted to natural language, making code "ephemeral, malleable, and discardable". We look at how this empowers non-coders and allows professionals to build custom tools like tokenizers in minutes.• LLM Agents & GUIs: Why Claude Code and Gemini Nano banana represent a new frontier where AI lives on your local computer and communicates through visual interfaces rather than just text consoles,.• The Death of Benchmarks: As labs "benchmax" through synthetic data and RLVR, Karpathy warns that crushing benchmarks no longer equates to reaching AGI,.As Karpathy notes, the industry has likely realized less than 10% of the potential of current LLM capabilities. Whether you're a developer or an AI enthusiast, these shifts represent a "terraforming" of the software landscape.--------------------------------------------------------------------------------To understand the shift from RLHF to RLVR, think of it as the difference between a student trying to please a teacher (who might be inconsistent or biased) versus a student solving a Rubik's cube. With the cube, the success is objectively verifiable, allowing the student to practice and improve for much longer periods without needing constant human feedback.