RK's Rambling

Disclaimer: As a Google Developer Expert (GDE), I was incredibly fortunate to be invited by Google DeepMind to test these models internally before their public release. The capabilities I'm sharing today are based on my hands-on early access. Have you ever stared at a dense, 15-page academic paper and wished you could just see what the researchers were talking about? As someone who frequently reads and writes heavy technical research, I face this constantly. Today, Google is introducing Nano Banana 2 (Gemini 3.1 Flash Image) . It is the latest state-of-the-art image model, and it is here to completely change how we interact with complex information. By bringing advanced world knowledge and reasoning to the high-speed Flash lineup, Nano Banana 2 dramatically closes the gap between lightning-fast generation speed and breathtaking visual fidelity. To put this to the test, I took two of my own highly technical research papers, uploaded the PDFs directly into the work...

 Author: Rabimba Karanjai Scope: Problem statement + data methodology + model training (no deployment discussion) Abstract Real‑time coaching in motorsport is a safety‑critical learning problem : a system must map noisy, high‑frequency telemetry to short, actionable guidance that remains physically consistent and avoids hazardous recommendations . This paper proposes a “Split‑Brain” training formulation that separates (i) a semantic coaching target (what action/critique should be expressed) from (ii) a reflexive interface (how actions are represented as compact, verifiable tokens). The approach trains a Small Language Model (SLM) in the Gemma family [1] using QLoRA fine‑tuning [2] , and introduces a telemetry tokenizer plus teacher‑student synthesis pipeline to generate instruction‑action pairs at scale. Core contribution: a reproducible method to convert “ golden lap ” differential tel...

" AI in a Jupyter notebook is safe. AI on a race track—at 150 mph—is a different story." - Ajeet Mirwani When we accepted the High-Velocity AI Field Test , the challenge was clear: Build an AI system that could coach a driver in real-time. But in motorsport, "real-time" doesn't mean a fast web request (500ms). It means sub-50ms . At 150 mph, a half-second of latency puts you 110 feet further down the track, the difference between hitting the apex and hitting the wall. We quickly realized we couldn't rely on the cloud for everything. We needed a "Split-Brain" architecture: Gemini Flash 3.0 in the cloud for high-level strategy, and a fine-tuned Gemma designed for immediate, reflex-based coaching. While we haven't put the model in the driver's seat just yet, we have successfully completed the critical first phase: engineering the "brain" capable of understanding high-speed telemetry. Here is the deep technical dive into ho...

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...

SpeakWise: Build an AI Public Speaking Coach with Gemma 3n Project by: Rabimba Showcase for: Google Developer Expert (GDE) AI Sprint Imagine having a private, real-time AI coach that watches your presentations, listens to your speech, analyzes your slides and gestures, and provides actionable feedback to help you improve confidently. Using Google’s new Gemma 3n, we built exactly that: SpeakWise , an AI-powered public speaking coach that leverages multimodal understanding to transcribe, analyze, and critique your talks—all while keeping your data private. Github Code. 🚀 Why Gemma 3n? Gemma 3n is Google’s open multimodal model designed for on-device, privacy-preserving, real-time AI applications . It is uniquely capable of: 📡 Simultaneously processing audio, image, and text, forming a holistic understanding of your talk. 🗂️ Following advanced instructions (“Act as a world-class presentation coach” and structuring output into clear, actionable insights). ...