What shipped & dropped across AI labs, today.
Today
Tue · Jul 21 · 4 posts
How Anthropic secures its AI-native software development lifecycle
Anthropic Deputy CISO, Jason Clinton, details how the Security Engineering team secures a SDLC that has AI authoring 80% of merged code.
Grok for Outlook
Use the Grok add-in for Microsoft Outlook to summarize long email threads, draft replies in your voice, and keep your inbox organized.
Gemini 3.6 Flash is now available in GitHub Copilot
Gemini 3.6 Flash, Google’s latest Flash model, is now rolling out in GitHub Copilot. It is designed for web and app development, coding, and longer-horizon agentic tasks. It has configurable…
How Datadog built a “universal machine tool” for Claude Code
Datadog has an agent write specifications for a deterministic kernel to write application code
Yesterday
Mon · Jul 20 · 6 postsAI credit pools for cost centers in the billing UI
You can now manage a cost center’s AI credit pool directly in the billing UI where you create and edit cost centers. Previously, you could only manage this through the…
Apply for Anthropic’s AI for Science rare disease research grants
Copilot users can now see AI credits used per billing cycle
Copilot Business and Copilot Enterprise users can now see how many AI credits they’ve used this billing cycle, even without an individual budget. Find this on your GitHub Copilot usage…
Run Ray on TPU, Part 1: The foundations
Ray 2.55 introduces official, first-class support for Google Cloud TPUs, enabling developers to run distributed Python workloads on Google's accelerators using the familiar Ray task-and-actor APIs. To handle the strict networking requirement of keeping multi-host TPU "slices" together over their Inter-Chip Interconnect (ICI), the KubeRay Operator on GKE automatically provisions and labels the underlying hardware layout. Ray Core utilizes these labels via its slice_placement_group() primitive to atomically reserve complete slices, allowing developers to deploy jobs through KubeRay, Ray Train, or Ray Serve simply by declaring a hardware topology (like "4x4") without writing custom placement code.
Working at the frontier: How Rakuten builds agents overnight with Claude Fable 5
Why Rakuten thinks Claude Fable 5 is a step change in model intelligence, transforming how long-running work is done.
Grok for Excel
Use the Grok add-in for Microsoft Excel to ask questions in plain English, write formulas, and run scenarios without leaving the workbook.
Friday
Fri · Jul 17 · 6 postsGitHub Copilot app now available in the usage metrics API
The Copilot usage metrics API now reports the GitHub Copilot app usage in the enterprise and organization 1-day and 28-day reports. This gives enterprise and organization admins visibility into the…
Repository-level GitHub Copilot usage metrics generally available
The Copilot usage metrics REST API now reports repository-level activity. Two new endpoints return a daily, per-repository breakdown of pull request activity for Copilot coding agent and Copilot code review.…
Copilot code review: Customization and configurability improvements
Copilot code review now utilizes a firewall, custom setup steps, and independent runner configurations. It now reads custom instructions from the head branch to allow for easy testing and validation…
Improvements to Cursor in Slack
Cursor in Slack now shares a plan before it starts, runs in multi-repo environments, and can work across channels and threads. #Interaction improvements Cursor now responds with a plan before it begins, so you can jump in and redirect early. As it works, it updates its status so you can follow each step. We also refined how Cursor's responses look in Slack. In-message buttons are gone, replaced by compact footer links. Tables, PRs, and artifacts now render more cleanly. #Multi-repo environment support From Slack, Cursor can now start in a named multi-repo environment instead of a single default repository. If your frontend, backend, and shared code live in separate repos, Cursor reads your request and targets the environment that gives it access to all of them. Mid-task, when Cursor needs a repo outside the current environment, it prompts you with a Switch repository button. Click it, choose the repo or environment, and Cursor picks up right where it left off. #Cross-channel workflows Cursor can now read from and send messages to other Slack channels and threads. During a task, it can pull context from elsewhere in the workspace and post updates back in the original thread or the relevant channel. Learn more in our Slack docs.
Zero risk isn't the job: a CISO's guide to agentic AI
Anthropic's Deputy CISO shares a four-question framework for assessing agentic AI risk, and walks through controls that keep agent deployments bounded and auditable.
Working at the frontier: How Cursor knew Claude Fable 5 was ready for the hardest 1% of problems
How Anthropic's Claude Fable 5 beat CursorBench and expanded what's possible for Cursor and agentic coding.
Thursday
Thu · Jul 16 · 4 postsAutomations in Grok
Describe a job once and Grok runs it on a schedule or when an email arrives, then reports back.
Evolving Spec-Driven Development: Conductor Now Supports Antigravity
Conductor has evolved from a Gemini CLI extension into a portable plugin, bringing conversational Spec-Driven Development (SDD) to ecosystems like Antigravity CLI and Claude. Rather than relying on strict command sequences, developers can now chat naturally with their AI assistant while it dynamically manages persistent markdown artifacts (like spec.md and plan.md) in the background. This update eliminates workflow friction while ensuring your repository remains a version-controlled, single source of truth for your project's architecture and state across different AI tools.
How Anthropic runs large-scale code migrations with Claude Code
A step-by-step guide to running large code migrations with AI agents — including Bun's million-line Zig-to-Rust port.
Building scalable AI agents with modular prompt transpilation
To resolve the scaling bottlenecks and runtime errors caused by monolithic system prompts, engineering teams should treat prompts as build artifacts by modularizing instructions into reusable templates. By running these modular "skill files" through a transpiler, developers can enforce static validation, catch missing dependencies at build time, and integrate prompt generation directly into their CI/CD pipelines. This deterministic approach prevents code drift and ultimately establishes a safe framework where agents can propose updates to their own logic via standard pull requests.