161 results
MAY 19, 2026 / AI
The Google Cloud and NVIDIA developer community is celebrating its first anniversary with 100,000 members and a renewed focus on providing builders with advanced AI infrastructure and resources. To accelerate development, the community offers curated learning pathways for mastering LLM optimization, GPU-accelerated data analytics, and monthly expert-led webinars. Moving into its second year, the initiative will expand to include hands-on labs, engineering events, and specialized content focused on the growth of agentic AI.
![0049-GDP-NVIDIA x Google Cloud Developer Community Blog _ Design [GDP-13]-May15_revised-01](https://storage.googleapis.com/gweb-developer-goog-blog-assets/images/0049-GDP-NVIDIA_x_Google_Cloud_Dev.2e16d0ba.fill-800x400.png)
MAY 19, 2026 / Cloud
Google announced the transition from assistive AI to independent agents, highlighting the launch of the Gemini 3.5 series and major updates to its Antigravity agent-first development platform. For mobile developers, the post introduces new Android CLI tools, the Android Bench evaluation leaderboard, and an automated Migration agent designed to rapidly convert various frameworks into native Kotlin code. Web development is also being transformed through Chrome DevTools for agents, the HTML-in-Canvas API, and the proposal of WebMCP, an open web standard that enables browser-based AI agents to execute complex tasks.
MAY 12, 2026 / AI
How to transition from stateless chatbots to production-grade agents capable of managing long-running enterprise workflows, such as HR onboarding, that span days or weeks. It introduces the Agent Development Kit (ADK) and its architectural shifts, specifically using durable state machines and persistent session storage to ensure an agent never loses context during "idle time" or server restarts. By leveraging event-driven webhooks and multi-agent delegation, the tutorial demonstrates how to build resilient systems that "sleep" during pauses and wake up to resume complex tasks with high reasoning accuracy.
MAY 4, 2026 / AI
Researchers at UCSD have successfully implemented DFlash, a block-diffusion speculative decoding method, on Google TPUs to bypass the sequential bottlenecks of traditional autoregressive drafting. By "painting" entire blocks of candidate tokens in a single forward pass rather than predicting them one-by-one, the system achieved average speedups of 3.13x, with peak performance nearly doubling that of existing methods like EAGLE-3. This open-source integration into the vLLM ecosystem optimizes TPU hardware by leveraging "free" parallel verification and high-quality draft predictions for complex reasoning tasks.
APRIL 29, 2026 / Cloud
Google Cloud has introduced a high-performance integration that connects Rapid Storage directly to PyTorch via the fsspec interface to eliminate AI training bottlenecks. By utilizing Google’s Colossus architecture and bidirectional gRPC streaming, the solution offers up to 15 TiB/s aggregate throughput and significant reductions in latency. These improvements allow developers to speed up total training time by 23% with zero code changes required beyond updating the storage bucket type.
APRIL 22, 2026 / AI
Google Cloud has introduced the Agents CLI, a specialized tool designed to bridge the gap between local development and production-grade AI agent deployment. The CLI provides coding assistants with machine-readable access to the full Google Cloud stack, reducing context overload and token waste during the scaffolding process. By streamlining evaluation, infrastructure provisioning, and deployment into a single programmatic backbone, the tool enables developers to move from initial concept to a live service in hours rather than weeks.
APRIL 21, 2026 / AI
The blog post outlines the transition of a brittle sales research prototype into a robust production agent using Google’s Agent Development Kit (ADK). By replacing monolithic scripts with orchestrated sub-agents and structured Pydantic outputs, the developers eliminated silent failures and fragile parsing. Additionally, the post highlights the necessity of dynamic RAG pipelines and OpenTelemetry observability to ensure AI agents are scalable, cost-effective, and transparent in real-world applications.
APRIL 16, 2026 / AI
MaxText has introduced new support for Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) on single-host TPU configurations, leveraging JAX and the Tunix library for high-performance model refinement. These features enable developers to easily adapt pre-trained models for specialized tasks and complex reasoning using efficient algorithms like GRPO and GSPO. This update streamlines the post-training workflow, offering a scalable path from single-host setups to larger multi-host configurations.
APRIL 14, 2026 / AI
The Google Cloud AI Agent Bake-Off highlights a shift from simple prompt engineering to rigorous agentic engineering, emphasizing that production-ready AI requires a modular, multi-agent architecture. The post outlines five key developer tips, including decomposing complex tasks into specialized sub-agents and using deterministic code for execution to prevent probabilistic errors. Furthermore, it advises developers to prioritize multimodality and open-source protocols like MCP to ensure agents are scalable, integrated, and future-proof against rapidly evolving model capabilities.
APRIL 7, 2026 / AI
TorchTPU is a new engineering stack designed to provide a native, high-performance experience for running PyTorch workloads on Google’s TPU infrastructure with minimal code changes. It features an "Eager First" approach with multiple execution modes and utilizes the XLA compiler to optimize distributed training across massive clusters. Moving into 2026, the project aims to further reduce compilation overhead and expand support for dynamic shapes and custom kernels to ensure seamless scalability for the next generation of AI.
