标签：#p

Google I/O 2026: MCP Is Now Infrastructure Google I/O used to be about new models. This year it was about what those models do - and how they connect to everything else. MCP was everywhere. Not as a novelty. Not as an experiment. As the assumed plumbing. Here's what actually shipped. Gemini Spark Will Run on MCP for Third-Party Tools The headline agent at I/O 2026 was Gemini Spark - a 24/7 AI agent that runs on cloud VMs, works while your devices are off, and handles long-running tasks across Gmail, Docs, and Calendar. Spark integrates with Google Workspace apps first, then expands to third-party tools via MCP over the summer. That's the part worth sitting with. Google built its flagship consumer agent and then said: for everything outside our walls, we'll use the open protocol. A year ago, MCP was a specification from Anthropic. Today, Google built its flagship consumer AI agent on it. Cursor, Copilot, Windsurf, Mistral, Grok - they all support it too. When the company that runs Search, Gmail, Android, and Chrome commits to MCP as the integration layer for its flagship product, the protocol debate is effectively over. Managed Agents Get MCP Servers by Default Google also launched Managed Agents through the Gemini API - a setup where a single API call provisions a remote Linux environment with its own isolated sandbox. Each agent gets its own ephemeral sandbox provisioned with skills, Model Context Protocol (MCP) servers, and server-side tools. Full integration with A2A and Agent Platform governance and security are coming soon. Managed Agents are powered by the Antigravity agent and built on Gemini 3.5 Flash. Developers can define custom agents through versionable markdown files such as AGENTS.md and SKILL.md, rather than building complex orchestration layers from scratch. This is Google offering hosted execution, sandboxing, state handling, and MCP tool access as a bundled service. The enterprise pitch is operational abstraction - you define the agent, Google runs

Probabilistic Graph Neural Inference for deep-sea exploration habitat design for extreme data sparsity scenarios Introduction: The Abyssal Classroom It was 3 AM, and I was staring at a screen filled with bathymetric data from the Mariana Trench—or rather, the absence of it. The dataset I had painstakingly compiled from oceanographic surveys, autonomous underwater vehicle (AUV) logs, and satellite altimetry had 97% missing values. My initial approach—a standard deep learning model for habitat design—failed catastrophically, producing predictions that were physically impossible (like habitats floating 200 meters above the seafloor). That night, as I watched the loss curve plateau into nonsense, I realized something profound: deep-sea exploration habitat design isn't just an engineering challenge; it's an inference problem under extreme uncertainty. My learning journey into probabilistic graph neural inference began that night. While exploring how to model the sparse, irregularly sampled data from hydrothermal vent fields, I discovered that traditional neural networks treat observations as independent, ignoring the inherent relational structure of the deep-sea environment. Through studying geometric deep learning and Bayesian inference, I realized that graph neural networks (GNNs) could capture the complex dependencies between seafloor features—but only if we could handle the missing data probabilistically. This article documents what I learned from building a probabilistic graph neural inference system for deep-sea habitat design, where data sparsity isn't a bug but a feature. Technical Background: Why Graph Neural Networks for the Abyss? Deep-sea habitats—from hydrothermal vent chimneys to cold seep mounds—are not randomly distributed. They form interconnected networks governed by geological processes, fluid dynamics, and biological colonization patterns. In my research, I found that this relational structure is perfectly suited for graph neural networks. However, th

Trisha Ballakur discusses her journey from a backend software engineer to CTO and CEO, using her startup Pointz as a case study. She explains how to implement bottom-up customer discovery to find product-market fit, effectively delegate to global contractors to reduce build times, customize open-source repos like Valhalla, and apply engineering test-case models to business development. By Trisha Ballakur

TP-Link has unveiled a Wi-Fi 8 router, but it's unclear if the company can sell it in the US after the recent ban on routers made outside the country.

Learn how to integrate model-based systems engineering (MBSE) with mission-driven requirements to create a connected framework that delivers reliable solutions designed with key objectives. submitted by /u/goto-con [link] [留言]

submitted by /u/Shadowys [link] [留言]

submitted by /u/daviddel [link] [留言]

submitted by /u/Mellowww [link] [留言]

I first realized there was an issue with Kia's flagship EV9 when I tried to unlock my car last year. The hulking three-row SUV was sitting on my driveway completely dead. The key didn't work, the app connection to the car was gone, and I was already late to an appointment. Luckily, I had prepared […]

It’s slimmer, lighter, and more durable. But the real upgrade is Oura’s push into AI-powered health insights and proactive monitoring.

Temu has been fined €200 million (about $232 million) by the European Commission after it found that consumers are "very likely to encounter illegal items" on the popular Chinese e-commerce platform. According to the commission, Temu breached Digital Service Act (DSA) rules by failing to identify and assess the systemic risks of illegal products being […]

submitted by /u/wineandcode [link] [留言]

I've been documenting some thoughts on stream plumbing in embedded systems, based on my own frustrations encountered while interfacing modules on constrained platforms. Mostly just an attempt to formalise patterns I've found to be working well recently. Interested in hearing how others approach this. submitted by /u/MickJC_75 [link] [留言]

Created from fabric scraps that would otherwise end up in a landfill, this thoughtfully constructed bag looks great and can carry everything you need for a weeklong trip.

New mothers working in software development are staring down an AI-pilled workplace they barely recognize.

Mac dynamic island with music, timers, clipboard, file drops Discussion | Link

Smart travel planning made effortless Discussion | Link

With sharper resolution and lightning-fast performance, Canon’s latest full-frame mirrorless punches well above its class.

The Centers for Research in Emerging Infectious Diseases were launched during the Covid-19 pandemic. The group lost its funding under Trump in part due to conspiracy theories.

This year, there’s been a wave of notable energy companies going public via IPO in the US. The solar and battery company Solv Energy went public in February, to the tune of $6 billion. X-energy, which is building small modular nuclear reactors, did the same in April, and its stocks surged on its first day…