News and Tech Update

A federal appeals court handed GitHub, Microsoft, and OpenAI an important win in the first major appellate ruling over how AI coding tools can use open-source code. As we all know, all the AI code-generating programs learned their lessons largely from open-source code. So a group of anonymous open-source plaintiffs argued that GitHub Copilot and OpenAI Codex were built using code from public GitHub repositories, including open-source licensed code. They argued the AI tools had generated code without the author attribution, copyright notices, and license terms that came with the original work. Now, the Ninth Circuit has returned a verdict in Doe vs. GitHub siding with Big AI. Ouch! But it may not be as troublesome as it first appears. The Ninth Circuit’s September 16 decision turns on a technical but consequential distinction: AI-generated code that lacks author names, copyright notices, and license information is not necessarily the same as code from which that information has been...

Your team probably spends considerable effort securing your application. WAFs, rate limiting, input validation, penetration testing. Then code passes through a CI/CD pipeline that runs with privileged credentials, executes arbitrary code from third-party actions, has broad network access to internal systems, and logs secrets in plain text if someone makes one configuration mistake. The pipeline that builds and deploys your secure application is itself the least secure system in your infrastructure. Think about what a CI/CD pipeline actually is from an attacker’s perspective. It is a system that automatically executes code, often with administrative access to your cloud accounts, container registries, databases, and production environments. It pulls dependencies from the public internet. It runs plugins and actions written by strangers. It often has access to every secret needed to deploy to every environment. If you designed a system specifically to be an attractive target for s...

Anyone who has run more than one Claude Code session at a time on the same codebase already knows the drill. You split the work yourself, decide which session touches which files, track what each one is doing, and then stitch the results together by hand. The model can write good code in parallel. Coordinating that parallel work has been left entirely to humans. Anthropic is trying to close that gap. On September 17, the company rolled out a redesign of Claude Projects that moves it from a static container for files and chat history into something closer to a project manager: A coordinator that breaks a stated goal into parallel threads, delegates the work, and assembles the results. The mechanics are straightforward once you see them. A user states a goal and connects the relevant repositories or context, and a coordinator thread scopes the work and spins up worker threads to handle pieces of it. Each of those threads runs as a full Claude Code cloud session on its own branch, and c...

Anthropic announced it has redesigned Claude Projects to coordinate multiple coding sessions in parallel, with the new experience debuting in beta for Claude Code users. In the redesigned Projects, a coordinator can break a development goal into separate threads, assign them to individual Claude Code cloud sessions and track their progress from one shared project. The beta is initially available to select Claude Pro and Max subscribers using Claude Code cloud sessions, with wider Pro and Max access coming over the next week. Updated Projects across the rest of Claude and on Team and Enterprise plans will follow. The naming may be confusing because Anthropic already uses “Projects” for its general-purpose Claude workspaces. Anthropic describes this as a new version of that existing feature, rather than a separate Claude Code product. The redesign is debuting in Claude Code, while existing Projects in chat and Cowork will continue to work for now and be upgraded as the rollout expands....

StackHawk this week launched Wingman , an artificial intelligence (AI) tool that makes it possible for application developers to automatically fix vulnerability issues as code is being written. Wingman is designed to install into Claude Code, Cursor, GitHub Copilot, Codex, and Antigravity. It scans the live application, interprets findings, and fixes vulnerabilities in a way that makes it simpler for application developers to maintain context as they write code. At the core of those capabilities, StackHawk’s application testing platform is made available through a set of AI skills, hooks and rules that Wingman invokes. Once an AI coding agent finishes a feature, Wingman auto-configures and boots the running application to run a series of security tests with no manual steps required. Findings are then shared with the AI coding agent that wrote the code to fix any issues discovered. Wingman then rescans that application to confirm the fix held before reporting back to the continuous in...

Harness today previewed a revamped user interface for its platform for managing software deployments that makes it simpler for software engineers to manage teams of artificial intelligence (AI) agents using a forthcoming Harness Software Factory platform. Speaking at an {unscripted} NYC 2026 event , Harness CEO Jyoti Bansal told conference attendees the Harness Software Factory will make it simpler for DevOps teams to enforce standards across agentic AI engineering workflows. Rather than allowing AI agents to reinvent the way an application is developed every time, the Harness Software Factory makes use of specifications to enforce standards in a way that both applies controls and ultimately reduces the number of tokens that might otherwise be consumed. Harness also previewed Vibe Mode for Harness, an interface for the Harness platform that applies policies to code being developed by so-called citizen developers who are generating code using AI tools. Finally, Harness revealed a Fle...