What shipped & dropped across AI labs, today.
Yesterday
Fri · Nov 6 · 5 posts
Copilot code review: API management and review overview updates
You can now manage GitHub Copilot code review settings through the REST and GraphQL APIs, from enterprise policy down to individual repositories. Copilot’s review overview comment is now more compact,…
GitHub Copilot weekly releases — November 2
This week, Copilot comes to JetBrains Air and Xcode’s coding assistant, with updates to help you manage pull requests, settings, and agent sessions. GitHub Copilot app Set up Agent Merge…
Copilot impact dashboard adds chat and VS Code agent usage
The Agent adoption chart in the Copilot impact dashboard now includes Copilot Chat and the VS Code Agents window, so you can see engagement across nine Copilot experiences. The adoption…
Voice Chat with Agents
You can now talk with your agents in a realtime voice call. Describe a change out loud, ask what an agent is working on, and hear updates as the work moves forward. Voice chat works in the Agents Window, the IDE, on web, and in Cursor for iOS. #Start a call When the agent is idle and the chat input is empty, click the waveform button labeled Start voice chat. It sits where Send usually is, next to the microphone button for dictation. On iOS, you can also say "Hey Siri, call Cursor" to start a call with a new agent, and mute or hang up from the Lock Screen with a Live Activity. #Talk while the agent works The voice agent listens, replies, and hands coding work to the agent in your chat. While that agent runs, you can keep the conversation going. Ask what changed in a diff, check on a run, search earlier messages and calls with this agent, or switch between workers in a Project that has them. The coding agent speaks for itself too. It shares short progress notes while it works and a brief answer when it's done, so you usually hear a short update instead of the full written reply. #Powered by Grok Voice Voice chat runs on Grok Voice. Pick from many voices in Voice settings, including Ara, Eve, Leo, Rex, and Sal. With Privacy Mode on, voice chat runs with zero data retention. #Voice chat and dictation Voice chat is separate from dictation, which earlier releases called Voice Mode. Dictation still turns your speech into text in the chat input, and Cmd+Shift+Space still toggles it in the IDE. Voice chat is a two-way conversation where the agent answers out loud. Update Cursor to try voice chat. Learn more in our docs.
Agent Distraction
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 creep on open bugs (57%). Higher-capability models show higher distraction rates, highlighting the need for automated cleanup and linters to maintain agent-friendly codebases
Thursday
Thu · Nov 5 · 3 postsUsage metrics API flags telemetry and version gaps
The Copilot usage metrics API now shows which users are missing client telemetry or are running IDE or extension versions below the usage metrics minimums. New per-user fields appear in…
Usage metrics API adds code review details and addressed rate
The enterprise and organization repository-level Copilot usage metrics reports now show what happens to Copilot code review feedback. A new copilot_code_review object on each repos-1-day row reports how many Copilot-reviewed…
Build Live Apps in Cursor
Agents have lowered the cost of writing code, but most apps and tools that users build never make it off of localhost. Now, you can build live apps directly in Cursor and share them with a link. Describe what you want to create and Cursor will handle the rest: build the app, set up a database, configure auth, and deploy it. With this release, anyone in your org can now build useful tools for their work and share them with teammates. Update Cursor to try it out. #How it works To get started, kick off a cloud agent and tell it explicitly that you want to build an app. Cursor will scaffold the web app, spin up a Postgres database if needed, set up authentication behind Cursor SSO, and provision hosting. If the app needs to connect to your systems, like a data warehouse, Cursor will ask for the secrets it needs. When you go to publish your app, you pick a unique domain and Cursor gets you back a live link. At Cursor, users across sales, operations, legal, finance, and engineering are building internal apps. Some of the most useful ones are: Internal ops tools for event registration, account assignments, and desk seating Multimedia presentations for customers that combine slides, videos, and interactive demos Training, enablement, and onboarding experiences for new hires Live dashboards and tools for sales teams to understand their pipeline and opportunities Portals to manage engineering services Cursor can deploy apps from new or existing repos across Cursor Origin and GitHub. Support for other git providers like GitLab is coming soon. #Controls and guardrails When you publish an app, Cursor will run deterministic and agentic security checks to catch common issues, like secrets written in app files or exposed credentials. If a check fails, the agent tells you what it needs to fix. Nothing goes live until the checks pass and you explicitly publish the app. Team admins can see every app and secret on the team. They can also add their own rules to the security review, approve publishes the review blocked, and unpublish any app. #Share with your team Once the checks pass, Cursor deploys the app to the URL you chose. You can then share your app with specific people, groups, or your entire team, all behind Cursor sign-in. On individual plans, you can also share apps publicly. You can also provision a Cursor API key if you want a script or Grok Bot to use your app. #Get started This capability is now available on all paid plans with cloud agents enabled. Learn more by reading our docs.
Wednesday
Wed · Nov 4 · 1 postTuesday
Tue · Nov 3 · 3 postsBYOK and enterprise custom models in the Copilot usage API
The Copilot usage metrics API now shows bring your own key (BYOK) usage across GitHub Copilot, so you can separate it from Copilot-hosted model usage. Reports cover two kinds of…
Cost per task: Using cheap classifiers to route across Gemini models and thinking levels
Per-token API prices keep falling, yet engineering bills for autonomous coding agents keep climbing....
GitHub Copilot is now available in JetBrains Air
You can now delegate coding tasks, guide execution, and review results with GitHub Copilot in JetBrains Air. The integration uses the Agent Client Protocol (ACP) and supports tasks in your…
Monday
Mon · Nov 2 · 1 post
Sunday
Sun · Nov 1 · 2 posts
Gemini Omni 1.2 Flash: Fast and cost-effective with longer clips
Agentic cheminformatics with Google Antigravity
October 29
Thu · Oct 29 · 1 postOctober 28
Wed · Oct 28 · 4 posts
AI for Science expands to foundations and research institutions
Repository plugin defaults and enterprise plugin controls generally available
Repository plugin defaults and enterprise-managed plugin controls are now generally available in VS Code, Copilot CLI, and the GitHub Copilot app. Trusted repositories can install and enable default plugins for…
Smaller apps and faster GPU inference with LiteRT in Google Play services
LiteRT in Google Play services now offers GPU inference powered by ML Drift, delivering about 1.4x faster performance alongside the new CompiledModel API. Switching from bundled binaries reduces app size by approximately 8 MB while enabling seamless, automatic runtime updates across billions of devices. Developers can easily adopt these updates by updating their dependencies and utilizing the new Android Studio LiteRT skill for automated migration.
Hill-Climbing Agent Quality with Agents CLI Evaluation
This post walks through the Agents CLI evaluation loop for systematically improving agent performance through continuous testing and grading. It demonstrates how to catch prompt regressions, evaluate model backend swaps using side-by-side trace comparisons, and automate evaluation passes. Developers will also learn how to integrate nightly scheduled runs with threshold-based alerting to track quality over time.