Search - Google Developers Blog Community/Events Learn Blog YouTube Search Community/Events Learn Blog YouTube Search 2786 results Clear filters Filter by Content Type Select all Announcements Beginner Best Practices Business and Leadership Case Studies Code Health Community Documentation Events Explore Hard How-To Guides Industry Trends Intermediate Learn Performance Problem-Solving Project Management/Agile Q&A Release Notes Solutions Testing TotT Tutorials Product Select all Ads AI Edge Android Angular AR Assistant Auto Checks Chrome OS Chrome Web Cloud DeepMind Design Engineering Firebase Flutter Gemini Gemma Geo Go Google AI Studio Google Labs Google Pay IDX Kaggle Keras Matter Pay Play Search Smart Home TensorFlow TV Wallet Wear OS Workspace Technology Select all AI Cloud Mobile Web JULY 16, 2026 / AI Evolving Spec-Driven Development: Conductor Now Supports Antigravity Conductor has evolved from a Gemini CLI extension into a portable plugin, bringing conversational Spec-Driven Development (SDD) to ecosystems like Antigravity CLI and Claude. Rather than relying on strict command sequences, developers can now chat naturally with their AI assistant while it dynamically manages persistent markdown artifacts (like spec.md and plan.md) in the background. This update eliminates workflow friction while ensuring your repository remains a version-controlled, single source of truth for your project's architecture and state across different AI tools. JULY 16, 2026 / AI Building scalable AI agents with modular prompt transpilation To resolve the scaling bottlenecks and runtime errors caused by monolithic system prompts, engineering teams should treat prompts as build artifacts by modularizing instructions into reusable templates. By running these modular "skill files" through a transpiler, developers can enforce static validation, catch missing dependencies at build time, and integrate prompt generation directly into their CI/CD pipelines. This deterministic approach prevents code drift and ultimately establishes a safe framework where agents can propose updates to their own logic via standard pull requests. JULY 16, 2026 / AI Expanding Choice in Gemini Enterprise Agent Platform: Introducing Grounding with Parallel Web Search Google Cloud has partnered with Parallel Web Systems to natively integrate Parallel's search infrastructure as a web grounding provider on the Gemini Enterprise Agent Platform. This integration enables developers to anchor their AI agents in verifiable, real-time web results, significantly improving factual accuracy for complex enterprise workflows. Additionally, the partnership offers expanded architectural flexibility, allowing users to programmatically extract, permanently cache, and process web data alongside other large language models. JULY 14, 2026 Systems Engineering Playbook: Optimizing Qwen 3.5-397B MoE on Ironwood (TPU7x) To serve the 397B-parameter Qwen 3.5 Mixture-of-Experts (MoE) model on Ironwood TPUs, engineers developed a modular JAX/Pallas optimization stack that achieved up to a 4.7x inference speedup for prefill-heavy workloads. The team bypassed severe hardware sharding constraints by deploying a hybrid Data Parallelism and Expert Parallelism (DP+EP) topology, paired with custom low-level communication fusions like a hierarchical reduce-scatter to optimize cross-device token routing. Finally, by executing hardware-aware custom kernels—such as Batched Ragged Page Attention and a fully-fused Gated DeltaNet (GDN) block—they successfully saturated HBM bandwidth and TensorCore MXUs to push system throughput near its theoretical roofline limits. JULY 9, 2026 / Web LiteRT.js, Google's high performance Web AI Inference We're excited to introduce LiteRT.js, the newest member of the LiteRT family! LiteRT.js is our powerful solution for running machine learning models directly in the browser, extending Google's cross-platform edge AI runtime to the web. Built for JavaScript developers, LiteRT.js delivers state-of-the-art ML model inference performance on WebGPU and upcoming WebNN, with a fallback to WebAssembly for CPU. This post provides a quick tour of LiteRT.js and gives web developers everything they need to get started. JULY 8, 2026 / Mobile Bridging the Domain Gap: AI Race Coach built with Antigravity and Gemini On May 23, 2026, fresh off the stage at Google I/O, our Google Developer Experts (GDEs) converged on... JULY 6, 2026 / AI We terminated a TPU mid-training and it recovered in seconds: Introduction to elastic training with MaxText Distributed AI training is notoriously fragile because losing a single machine typically crashes the entire multi-node job, forcing a time-consuming, full-workload infrastructure restart. To address this, Google’s JAX ecosystem utilizes elastic training via Pathways, which converts a hardware failure into a catchable Python exception so the running process can survive. When an unplanned failure occurs, the system automatically replaces only the broken worker, restores the last viable checkpoint from Cloud Storage, and resumes training in place—minimizing total downtime to under two minutes without ever restarting the main controller process. JULY 1, 2026 / AI Why we built ADK 2.0 Answering the questions of "why we built ADK 2.0". This explains the rationale, some of the features, and why a developer should consider upgrading. This will be published the day after ADK go 2.0 launches. JULY 1, 2026 / AI Build agentic full-stack apps with Genkit The open-source Genkit framework has introduced the Agents API, a full-stack tool designed to simplify the complex plumbing of conversational AI by packaging message history, tool loops, and streaming into a single interface. The API supports flexible, server- or client-managed state persistence—allowing for advanced workflows like history branching, long-running detached tasks, and multi-agent coordination—while seamlessly connecting backends to frontends via a unified wire protocol. Currently available in preview for TypeScript and Go, it also integrates with the Genkit Developer UI to allow developers to easily test, debug, and inspect agent snapshots without writing client code. JULY 1, 2026 / AI ML Development in VS Code with Google Cloud Power: Workbench Extension Now Available The Google Cloud Workbench Notebooks extension for VS Code has officially launched, allowing developers to connect their local IDE to scalable, cloud-based Jupyter environments. This integration streamlines the machine learning lifecycle by eliminating context switching and providing direct access to high-performance Google Cloud infrastructure. To support transparency and community-driven innovation, the newly released extension is fully open-sourced and available on GitHub and the VS Code Marketplace. 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