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Most Read Today

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  1. 1GrapheneOS duress PIN could land a man in prisonGrapheneOS duress PIN could land a man in prisonMobile·1 min read·59 views
  2. 2Systems Engineering Playbook: Optimizing Qwen 3.5-397B MoE on Ironwood (TPU7x)Systems Engineering Playbook: Optimizing Qwen 3.5-397B MoE on Ironwood (TPU7x)Technology·1 min read·59 views
  3. 3How A2A is Building a World of Collaborative AgentsHow A2A is Building a World of Collaborative AgentsWorld·1 min read·57 views
  4. 4Build reliable multi-agent applications with ADK Go 2.0. Discover our new graph-based workflow engine, built-in human-in-the-loop, and dynamic orchestrationBuild reliable multi-agent applications with ADK Go 2.0. Discover our new graph-based workflow engine, built-in human-in-the-loop, and dynamic orchestrationTechnology·1 min read·49 views
  5. 5Rust reveals trailer for Global Warfare 4 eventRust reveals trailer for Global Warfare 4 eventWorld·1 min read·49 views
  6. 6A2UI + MCP Apps: Combining the best of declarative and custom agentic UIsA2UI + MCP Apps: Combining the best of declarative and custom agentic UIsTechnology·1 min read·47 views
  7. 7Expanding Choice in Gemini Enterprise Agent Platform: Introducing Grounding with Parallel Web SearchExpanding Choice in Gemini Enterprise Agent Platform: Introducing Grounding with Parallel Web SearchTechnology·1 min read·46 views

Featured Story

Building scalable AI agents with modular prompt transpilation
Featured StoryAI

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.

GGoogle Developers•August 20, 2026•1 min read
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🔥 Featured Today

Newsroom

Top Stories

Scaling Agentic RL: High-Throughput Agentic Training with Tunix
Technology

Scaling Agentic RL: High-Throughput Agentic Training with Tunix

Tunix is Google’s new JAX-native post-training library designed to eliminate TPU idling bottlenecks when training multi-turn, tool-using LLM reasoning agents. It maximizes hardware throughput by combining highly concurrent, asynchronous rollouts with a decoupled producer-consumer pipeline, ensuring the trainer is constantly fed even while agents wait on network I/O or environment steps. Additionally, Tunix provides plug-and-play abstractions and continuous macro-level profiling, allowing developers to easily integrate custom open-source environments and optimize complex distributed workflows without massive code rewrites.

Google Developers•August 20, 2026
Evolving Spec-Driven Development: Conductor Now Supports Antigravity
Technology

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.

Google Developers
Expanding Choice in Gemini Enterprise Agent Platform: Introducing Grounding with Parallel Web Search
Technology

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.

LiteRT.js, Google's high performance Web AI Inference
AI

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.

Google Developers•August 20, 2026
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August 20, 2026
Google Developers•August 20, 2026

Developer Releases

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  • NNext.jsThe React Framework·4 weeks agov3.0Stable
  • ⚛ReactThe library for web and native·3 weeks agov23.75-karatStable
  • ⬡Node.jsJavaScript runtime·3 weeks agov0.1.2-2773.beta.0RC
  • TSTypeScriptTyped JavaScript·Todayv2.0Beta
  • 🐳DockerContainer platform·Todayv2.0Beta
  • RRustSystems programming language·TodayLatestStable
  • PyPythonGeneral-purpose language·3 weeks agov1.1Stable