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InfoQ AIGitHubGoogle Launches LiteRT Tensor API for Building, Customizing, and Optimizing ML Models for On-Device Google has introduced the LiteRT Tensor API, a lightweight C++ API that allows developers to build, customize, and optimize machine learning models directly for on-device execution.

InfoQ AIGitHubPresentation: Beyond the Harness: Canvas for AI Agents Nimisha Asthagiri shares how applying proven product engineering and architecture principles creates a bounded canvas for AI agents.

InfoQ AIGitHubGoogle and Meta Open-source TorchTPU to Bring Native PyTorch Support to Google TPUs TorchTPU is an open-source project developed by Google in collaboration with Meta that provides a native PyTorch backend for Google TPUs.

InfoQ AIGitHubUber Builds MCP Gateway to Govern Access to 800+ Servers and 5,000+ AI Agent Tools Uber has built an MCP Gateway to standardize how AI agents discover and invoke internal services across more than 800 MCP servers and 5,000 tools.

InfoQ AIGitHubArticle: Systemic Approach to Memory, Knowledge, and Context in Agentic AI Architectures InfoQ Homepage Articles Systemic Approach to Memory, Knowledge, and Context in Agentic AI Architectures

InfoQ AIGitHubAWS Capacity Largely Sold out through 2027, with On-Demand Headroom Reserved Amazon CEO Andy Jassy says AWS’s large committed cloud capacity is largely sold out through 2027, with part of 2028 already allocated, while the company continues to reserve headroom for On Demand…

InfoQ AIGitHubGoogle Discloses PageBreak AI Security Agent That Found 500+ XSS Bugs According to a paper published by Google, its AI-powered security agent has uncovered more than 500 XSS vulnerabilities across its web applications since entering full operation in January.

InfoQ AIGitHubDoorDash Cuts Grocery Reorder Latency from 16 to 2 Seconds with AI Agents DoorDash reduced grocery reorder latency from 16 to 2 seconds by precomputing recurring purchase needs using batch LLM inference and deterministic validation.

InfoQ AIGitHubNetflix Treats GPU Reliability as a Distributed Systems Problem Netflix has detailed how it is building a reliability platform for large-scale GPU training and inference, using layered health checks, automated remediation, and fault-tolerant training to keep jobs…

InfoQ AIGitHubPresentation: The Ladder is Missing Rungs: Engineering Progression When AI Ate the Middle Alasdair Allan discusses empirical research from METR and Anthropic on AI coding adoption. He explains the "supervision paradox," where reliance on AI tools disrupts junior skill development, risks…

InfoQ AIGitHubArticle: InfoQ AI, ML and Data Engineering Trends Report - 2026 This InfoQ Trends Report offers readers a comprehensive overview of emerging trends and technologies in the areas of AI, ML, and Data Engineering.

InfoQ AIGitHubPodcast: AI, ML, and Data Engineering InfoQ Trends Report 2026 In this episode, members of the InfoQ AI and ML editorial staff and friends of InfoQ discuss the current trends in the domain of AI, ML and Data Engineering.

InfoQ AIGitHubBug0 Releases QA Agent to Automate Regression Testing with Cached Playwright Replays QA Agent is a new source-available Playwright library for agentic browser regression testing. The agent associates test instructions expressed in natural language to actions on the user interface…

InfoQ AIGitHubHow to Make Artificial Intelligence More Environmentally Sustainable IT is responsible for about 2 - 4% of all global emissions, and electricity use at Microsoft and Google has climbed because of AI, according to their own reports.

InfoQ AIGitHubQCon AI New York 2026 Program Committee Picks: Agent Identity, Context Costs, and Open-Source Infere Who authorizes an agent’s actions, and how should teams manage growing context costs? The QCon AI New York 2026 program committee shares six session picks that tackle these questions, alongside…

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An agent writes the code now, and a human thumb hovers over Accept all a hundred times a day. The honest question of this decade is what, exactly, you just accepted. So I read the diff.

Eight years as a backend engineer in Stockholm, back in Tokyo since 2023, and I still ship something small most nights, usually badly. My one rule: the only real review of a model is the thing you built with it. That is why there is no leaderboard on this site.

Around a hundred and forty sources come through — the labs' own posts, the papers, the tools, the runtimes and languages nobody is watching yet, the chip under the demo, and the newsletters people actually forward. What is moving gets counted so the week reads at a glance, and every few days I explain one mechanism in plain words.

A story here is a headline, a short excerpt, one image and the link to whoever wrote it. We never reproduce the post; the images belong to their authors. A post written in Japanese appears in Japanese, exactly as it was published, and the link goes to the original.

— Yuki Halvorsen, Tokyo