Browse past weeks of engineering reads.
Enable on-device AI models to coordinate complex tasks across external data sources while maintaining persistent user context and proactive engagement without relying solely on cloud connectivity.
AI agents needed a standardized way to generate UI components that work across different platforms and frameworks without being tightly coupled to any specific technology stack.
Building production-grade AI agents that can maintain context and state across long-running enterprise workflows spanning days or weeks without losing information during idle periods or server restarts.
Mobile developers faced performance and battery inefficiency when running AI models on CPU/GPU, limiting real-time AI applications on edge devices.
Developers needed a unified way to build, deploy, and run high-performance machine learning models directly on edge devices (Google Pixel TPU) with reliable fallback mechanisms.
Autoregressive LLM decoding suffers from sequential bottlenecks where tokens must be generated one-at-a-time, limiting throughput and inference speed on hardware accelerators like TPUs.