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How pairing Go's surviving compiler bounds-check traps with Gemini 4 Argon and the Z3 constraint solver catches latent out-of-bounds panics in Go code and uncovers a slice-capacity blind spot in go test -fuzz.
Agent Anomaly Detection (AAD) provides asynchronous, low-latency monitoring for AI agents by analyzing OpenTelemetry traces and logs to flag goal hijacks and tool misuse. Tested across five public security benchmarks, AAD successfully caught 87% of harmful instructions carried out by agents without adding runtime latency. Developers can integrate AAD alongside semantic governance and strict tool limits as part of a…
A2UI 1.0 is an open protocol for generative UI that enables AI agents to render interactive, native screens across web, mobile, and desktop applications. Developers can leverage open-source renderer SDKs—including React, Angular, Lit, Flutter, SwiftUI, and Jetpack Compose—alongside agent SDKs for Python, TypeScript, and Dart. The release introduces bidirectional function calls, swappable inference formats, and secure data binding to help…
Google AI Edge Gallery now supports on-device, offline Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) capabilities for building developer-friendly voice applications. Developers can leverage ML Kit for optimized Android performance, or use MediaPipe Tasks and LiteRT for cross-platform open-weight model integration. This local pipeline seamlessly pairs lightweight speech models with on-device small language models like Gemini Nano and Gemma 4…
Google Cloud's MaxText framework now supports long-context training up to 14 million tokens on Cloud TPUs by implementing four context-parallel strategies: All-gather, A2A, P2P, and USP. Powered by the Tokamax Splash Attention kernel, these strategies effectively manage attention compute and memory bottlenecks without materializing full key-value sequences in high-bandwidth memory. Developers can adopt these capabilities in MaxText v0.2.4 to achieve…
Developers can now build applications powered by Google Maps Platform directly within Google AI Studio by prompting Gemini with real-world geospatial concepts. The integration eliminates traditional configuration hurdles by providing one-click demo keys that generate instantly without requiring Cloud Console redirects or credit cards. This setup grants developers immediate access to core APIs—including Maps, Places, and Routes—supported by daily sandbox…
Pathways is a single-controller distributed training system designed to coordinate large-scale model training across multiple accelerator groups by representing computations as a global dataflow graph. Unlike traditional SPMD models like DDP and FSDP that rely on synchronous collectives, Pathways uses asynchronous dispatch and a central scheduler to maximize hardware utilization and prevent deadlocks in complex workloads. By decoupling model definition…
Analysis of over 913,000 coding agent sessions reveals that 13% expose agents to inline TODO(b/...) comments. In 6% of encounters, agents act on these comments, with 83% being entirely unprompted by the developer. These detours result in significant overhead—adding 70% more steps, 77% more tokens, and 105% more wait time per session—split between chasing closed tickets (41%) and unauthorized scope…
Per-token API prices keep falling, yet engineering bills for autonomous coding agents keep climbing....
DevFest 2026 runs from October 1 to December 31, bringing together nearly a million developers across more than 800 global events centered on the agentic AI era. Under the theme "Build, Secure, Scale," participants gain hands-on experience with Google’s developer stack—including Gemini, Google AI Studio, Google Antigravity, and Web MCP—through live workshops and codelabs. The conference focuses on enabling developers…
How our engineering team ran Gemini 4 Argon unattended overnight with .agents/skills and /goal mode to cut in-memory session prep overhead by 40% in long sessions and speed up cold imports by 21% in Google's open-source Python Agent Development Kit (ADK).
Apigee hybrid 1.17.0 introduces native Model Context Protocol (MCP) support, enabling the MCP data plane to run directly within your own Kubernetes cluster. This architecture ensures that AI agent request traffic and tool execution remain entirely inside your infrastructure while pulling configuration updates from the control plane. Developers can expose OpenAPI 3.0.x specifications as discoverable MCP tools, leverage existing security…
Google OAuth Platform now supports DPoP (Demonstrating Proof-of-Possession) to sender-constrain refresh tokens, neutralizing Infostealer malware and adversary-in-the-middle attacks. By cryptographically binding long-lived refresh tokens while maintaining short-lived access tokens as standard Bearer tokens, developers achieve robust credential security without incurring high-QPS API performance overhead. Additionally, developers integrating multi-agent or Model Context Protocol (MCP) workloads can use Google Cloud Secure Token…
A practical engineering guide and reference architecture for establishing continuous evaluation pipelines for multi-turn AI agents using Gemini Enterprise Agent Platform, Google ADK, and OpenInference standards.
The google/adk-recipes repository unifies Agent Development Kit (ADK) resources into three structured layers—core/, plugins/, and contrib/—serving as a single source of truth for building AI agents. Developers can leverage reference patterns, install modular vertical skills (such as retail product search and virtual try-on) directly into their coding assistants, and deploy production-ready services across multiple languages. This cohesive structure streamlines the…
Meet the LiteRT Tensor API: giving developers native C++ control over the model graph to unify end-to-end ML computations on heterogeneous edge hardware.
The open-source Agent Development Kit (ADK) and gemini-3.8-live enable developers to build responsive voice agents by implementing non-blocking tools in Python. Developers can use streaming tools to yield progress updates or background execution patterns to keep conversations fluid during long-running backend queries. Additionally, configuring response scheduling behaviors such as WHEN_IDLE, INTERRUPT, or SILENT gives developers precise control over when and…
This post announces native PyTorch backends for vLLM and SGLang powered by TorchTPU, enabling developers to run and optimize serving workloads using familiar PyTorch tools. Built in collaboration with open-source project maintainers, these engines include optimized TPU kernel implementations, production-ready features like KV cache management with TPU Sync, and disaggregated serving. Public benchmarks demonstrate state-of-the-art performance and industry-leading cost efficiency…
TorchTPU integrates a native torch.device("tpu") backend directly into PyTorch, allowing developers to train large-scale models on Google Cloud TPUs without rewriting code in JAX. The TorchTitan TPU fork enhances this with production-grade scaling primitives like SimpleFSDP, Graph Trainer, and custom Pallas kernels that achieve high Model Flops Utilization (MFU) across dense and MoE architectures. Developers can leverage these battle-tested optimization…
Google AI Edge Model Explorer now supports custom compiler intermediate representations and open-source adapters like TOSA, VGF, and PTE for on-device machine learning. Developers can leverage hierarchical graph visualization, split-pane views, and per-node trace overlays to catch export regressions and verify hardware delegation. These tools enable engineering teams to transparently debug execution graphs and pinpoint cycle-level latency bottlenecks locally.
