What shipped & dropped across AI labs, today.
Today
Mon · Oct 26 · 1 postFriday
Fri · Oct 23 · 6 postsDonating Agent Skills to the Agentic AI Foundation
Partnerships
Policy
Physicist Christopher Stubbs joins Anthropic's Long-Term Benefit Trust
Usage metrics API adds usage from tools like OpenCode
The Copilot usage metrics API now reports model usage from more of the tools your developers use with Copilot, including tools like OpenCode that weren’t previously captured in your reports.…
Turn notifications into code with GitHub Copilot in Teams
The new GitHub app in Teams brings notifications and Copilot together, helping teams turn issues into fixes and ship code faster without leaving the conversation. Get notified in channels, group…
Thursday
Thu · Oct 22 · 5 postsCustom skills, agents, and MCP servers in usage metrics API
Copilot usage metrics now show the names of the skills, custom agents, Model Context Protocol (MCP) servers, slash commands, and plugins that your organization creates, instead of grouping them under…
GitHub Copilot in Visual Studio — September update
September 2026 brought deeper visibility into your work: explore pull requests without leaving chat, bring the AI model your team trusts, and fix vulnerable packages the moment Visual Studio flags…
Global default for Copilot features generally available
In September, we announced a global default policy for generally available GitHub Copilot features and supported client capabilities on Copilot Business and Copilot Enterprise plans. Starting today, we’re gradually rolling…
Industry comparisons in the Copilot impact dashboard
The Copilot impact dashboard now includes an “Industry comparison” section. Enterprises, and organizations that aren’t owned by an enterprise, can compare their Copilot agentic adoption and leverage with aggregated benchmarks…
Architecting Governed, Self-Evolving AI Agents on Google Cloud
Learn how Project Chrysalis combines Google Cloud's Gemini Enterprise Agent Platform, Agent Development Kit (ADK), BigQuery, and a four-gate defense-in-depth security stack (Model Armor, Cloud KMS, Binary Authorization, and OPA) to build governed, self-evolving AI agents that remediate live production outages in 1.8 seconds.
Wednesday
Wed · Oct 21 · 1 postTuesday
Tue · Oct 20 · 4 postsBringing Visual Graph Debugging to Custom Edge Compilers with Model Explorer
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.
Announcing New vLLM and SGLang Inference Engines on TPU
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 on Ironwood TPU across long-context and agentic workloads.
Integrating TorchTPU into TorchTitan and Optimizing Models for TPU
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 playbooks, recipes, and the Hugging Face modeling backend to efficiently scale their own workloads on TPU infrastructure.
Unifying Edge AI: How Coral NPU’s open standard brings portable, energy-viable intelligence from model to silicon
Google Research has completed the 32-bit open-source Coral NPU RISC-V architecture with the introduction of its Matrix Execution Unit, which delivers up to 1 TOPS while targeting 10 mW average power for edge inference. Alongside this architecture, the release of the v1.0 open-source IREE/MLIR compiler allows developers to take models from JAX, PyTorch, and TensorFlow straight to Coral NPU hardware without writing custom assembly. Developers can now prototype and deploy these multimodal edge applications using new hardware options like the Synaptics Astra SR200 MCU series and globally available Coralboard devkits.12
October 19
Mon · Oct 19 · 3 postsGitHub Copilot models deprecated on October 19, 2026
As of today, October 19, 2026, we have deprecated the following models across most GitHub Copilot experiences (including Copilot Chat, inline edits, ask and agent modes, and code completions). These…
Copilot model policy API for enterprise, orgs, and teams
You can now use the REST API to retrieve and update Copilot model policies for enterprises, enterprise teams, agent apps, and organizations. What’s new GET and PATCH endpoints are now…
Native Model Authoring for Edge with LiteRT Tensor API
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.