Beyond TurboQuant: The gemma4-nano Journey
How we achieved sub-1GB AI that outperforms Google's compression research. A deep dive into Q3_K_S quantization, mobile validation, and why smaller models run faster than their larger counterparts.
Read Research →Real-World Mobile Testing: Why Stock Models Fail
We tested Google's "mobile-ready" Gemma 4 on a high-end phone. It overheated. Our nano version stayed cool. Here's the technical breakdown of why size matters on mobile devices.
Read Full Article →Cloud Speed, Zero Latency: The Local AI Advantage
Why 20+ tokens/sec on a CPU feels faster than cloud AI. The physics of network latency vs local inference, and why offline-first architecture will win in the long run.
Read Full Article →Kaggle Gemma 4 Good: Building gemma4-turbo
Our submission to the Kaggle Gemma 4 Good Hackathon. How we built IQ4_XS quantized models with vision support, and what we learned about BF16 quantization pipelines.
Coming Soon →Pocket Ash: Your AI Companion, Truly Offline
The vision for mobile AI that doesn't require internet, doesn't cost money, and doesn't compromise on capability. Building the future of personal AI assistants.
Coming Soon →Google's "Mobile-Ready" Gap: Promise vs Reality
What does "mobile-ready" actually mean? Google says Gemma 4 is optimized for mobile, but real-world testing tells a different story. Where the industry went wrong and how indie developers are fixing it.
Coming Soon →