RK's Rambling

We are currently living through the “ Post-Reasoning ” phase of the AI hype cycle. By now, models like Gemini 2.0 and the latest iterations of Gemini have normalized the idea that machines can “think”—or at least, simulate a chain of thought that feels indistinguishable from reasoning. But as we push these architectures to their absolute limits, we are starting to see a plateau. It isn’t a plateau of competence; the models are brilliant. It is a plateau of certainty . In building applications on top of these models, I’ve noticed a recurring pattern. Developers (myself included) often assume that if a model fails to predict the right outcome, it’s a failure of intelligence. We assume we need a larger parameter count, a longer context window, or better fine-tuning. But there is a ghost in the machine that scaling laws cannot exorcise. It is the fundamental difference between not knowing and not seeing . The Architecture of Doubt To understand why our models, even state...

🚀 The Google Colab VS Code Extension Enterprise AI Without Enterprise Costs If you've been following my work on LLM-assisted code generation and AI reasoning , you know I'm always looking for ways to democratize AI development . During my recent work on cross-chain smart contract generation , I needed to rapidly prototype different transformer architectures for code translation. Previously, this meant juggling between local development, cloud instances, and Colab notebooks . Now? I'm running everything from VS Code with zero context switching. Here's what changed my workflow: Python - Loading Large Models on Free GPU Copy Code # Running this on a free T4 GPU in VS Code - no setup required from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load a 7B parameter model - impossible on most local machines model = AutoModelForCausalLM.from_pretrained( "codellama/ CodeLla...