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Boo! Now Get Off My Browser Tab!

On-Device WebGPU • Voice & Vision Agents • Halloween Build Rebuilding NVIDIA’s Nemotron Haunted House 100% Locally on WebGPU How an inspiring multi-GPU Python, FastRTC, and Omniverse driveway installation turned into a WebGPU haunted house—complete with 44-target ARKit pirate-plank deformation, TSL volumetric hellfire, YOLOv10s + ByteTrack costume roasting. Source: github.com/rabimba/monsterhouse-webgpu • Narrated Demo Video: monsterhouse-live-demo.mp4 (3m 42s) • Inspiration: NVIDIA Nemotron Halloween W hen the NVIDIA team published Boo! Now Get Off My Lawn! Building a Haunted House with NVIDIA Nemotron Open Models on Hugging Face, it immediately stood out as one of the most joyful physical AI builds of the year. Instead of putting another polite assistant inside a chat box, they projected a grumpy, splintered sunken pirate ship onto a real suburban garage door—giving it li...
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WebAI Badge Studio: Espruino at Google WebAI Summit

WebAI Summit • Hardware Hacking • On-Device WebGPU Skipping the Photo Booth Line at Google’s WebAI Summit: Reverse-Engineering the 4-Color Espruino ePaper Badge (and Building an On-Device WebGPU Studio for It) How a 45-minute portrait queue turned into a live USB-C hardware hack, a bit-exact 96,000-byte 2bpp BWYR codec, surgical .bootcde firmware patching, and an open-source WebAI Badge Studio running LiteRT-LM and MediaPipe over WebGPU. By Rabimba Karanjai • Live Studio: rabimba.github.io/webai-badge • Source: github.com/rabimba/webai-badge W hen Jason Mayes invites you to Google’s WebAI Summit, you already know the demos are going to run locally in the browser without a cloud GPU in sight. What I did not expect when walking into the registration hall was to be handed one of the coolest pieces of conference hardware I have ever worn: a custom Espruino 4-color ePaper smart badge ( ...
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Racecraft: Fine-Tuning Gemma 4 with DPO for Sub-150ms On-Device Coaching at 130 MPH

Project Koru • Deep Dive Racecraft: Fine-Tuning Gemma 4 with DPO for Sub-150ms On-Device Coaching at 130 MPH How we distilled expert human motorsport coaching into a 12MB Gemma 4 LoRA adapter using Direct Preference Optimization (DPO)—cutting NPU TTFT to 149ms and locking sub-5-word root-cause cues at high speed. By Rabimba Karanjai • August 2026 • 10 min read Figure 1: Empirical DPO Fine-Tuning Loss convergence curve (0.693 down to 0.141), reward margin expansion (+1.768), and NPU performance benchmarks on Google Tensor G5 / NVIDIA GB10. P icturing a car barreling into Turn 3 at Sonoma Raceway at 130 MPH gives you an immediate sense of scale. At that speed, you are traveling 190.6 feet every single second (58.1 meters/sec). Your brain is processing visual apex marks, tire slip audio, steering effort, and lateral G-forces in split-second 200–300ms reaction windows. If an AI coach speaks a typical convers...
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Racecraft: Tale of Car Sensors

Racecraft · Technical Deep Dive · Sensor Fusion & Gemma Generalization Fusing 100+ On-Device Sensors at 130 MPH , And Why Gemma Didn't Need Fine-Tuning Inside Racecraft's real-time telemetry engine: multiplexing AiM CAN bus, RaceBox BLE, OBDLink, and 6-DOF IMU streams on Android, zero-shot corner doctrine generalization, and benchmarking Gemma 4 E2B on Tensor G5 silicon. A few days ago after sharing early results from Racecraft (Project Koru) , a question popped up in one of the comments that immediately caught my attention: "Curious how your team handled real-time fusion of 100+ sensors on-device. Did Gemma need race-specific fine-tuning, or did general coaching logic generalize?" It's the exact right question to ask. When people hear "AI race coach running on a phone in real time at Sonoma Raceway," they usually picture one of two extremes: either a brittle set of hardcoded if (speed < 40) statements, o...
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