BrianFagioli writes: Audacity 4.0 has been released with a rebuilt Qt interface, a new clip-editing model, customizable workspaces, native high-DPI rendering, and a new AUP4 project format. The open source audio editor also adds ASIO playback and recording support to official Windows builds, while bringing major changes to recording, playback, plugins, and project handling. The overhaul comes with some tradeoffs. Audacity 4.0 currently lacks several features found in Audacity 3, including Time Tracks, Note and MIDI tracks, Mixer, Macro Manager and scripting pipe, VAMP and LADSPA plugin hosting, and play-at-speed. The developers say they plan to bring the missing functionality back in future releases.

Read more of this story at Slashdot.

Microsoft is announcing some major changes to Xbox Cloud Gaming today, less than a year after opening up cloud gaming to its Xbox Game Pass Premium and Essential tiers. While all Game Pass plans have included unlimited cloud gaming over the past year, from November this will be limited to just 15 hours on Ultimate, 10 hours for Premium subscribers, and a measly 5 hours for Game Pass Essential.

"We recognize that introducing limits is a meaningful change, particularly for players who regularly spend more time gaming through the cloud," says the Xbox team in a blog post announcing the changes. "We expect this upcoming change to impact four pe …

Read the full story at The Verge.

Uber is laying off 93 Washington state-based employees as part of a sweeping global restructuring that will eliminate roughly 10% of the company’s workforce.

The cuts heavily impact the transportation giant’s Seattle hub, targeting those assigned to the downtown engineering center as well as regional remote workers.

The job losses are detailed in a new Worker Adjustment and Retraining Notification (WARN) notice filed by the company with the state’s Employment Security Department, which lists impacted positions ranging from software engineers to senior managers.

Affected staff were notified Wednesday and will receive full pay and benefits through a 60-day notice period ending Nov. 2. The cuts hit technical roles particularly hard, with position titles including software engineers, product managers, data scientists, and a Seattle-based director of engineering.

Nationally, the cuts eliminate about 3,300 corporate jobs — roughly 10% of Uber’s global workforce — in the company’s largest round of layoffs since 2020. In a memo to staff, CEO Dara Khosrowshahi framed the restructuring as a move to eliminate middle-management bureaucracy, flattening reporting structures by reducing management layers and cutting “micro-teams” by nearly half.

As part of the overhaul, Uber is also clamping down on remote work, capping fully remote positions at less than 1% of its total workforce and requiring most employees to move near core office hubs like San Francisco and New York. Khosrowshahi noted that savings from the cuts will help free up capital to invest in core operations, including autonomous vehicle partnerships.

Uber first established its engineering outpost in Seattle in March 2015 to tap into the region’s pool of tech talent. The company expanded significantly in 2018 when it signed a 10-year lease for 115,000 square feet at downtown’s Second & Seneca building at 1191 Second Ave.

The latest release of the .NET-centric Uno Platform Studio adds isolated UI previews, reusable XAML snippets, more targeted AI prompting and a new default design system, extending the browser-based and agentic development workflow previously introduced.

Max Johnson, founder of the AI agency briix, regularly publishes practical AI guidance for business owners and founders to help them get the most out of AI. Max has used some of that know-how to streamline his own day-to-day work, turning what used to be a manual content creation process—researching topics, judging their relevance, and developing hooks and drafting scripts—into a single automated workflow using Claude Code. In this episode of Zero to Agent in 30 Minutes, he shows you how to do the same while remaining in the loop to make the final calls.

How to build a content creation agent

1. Give the system the context it needs to generate relevant content. Max started with a small knowledge base containing information about himself and his business, details about his audience, sample scripts, and notes on his writing voice. These files gave Claude Code reference material for evaluating topics and generating scripts that reflected his brand and style.
2. Explain the process in plain English and let AI take it from there. As Max pointed out, “Vibe coding is describing what you want clearly and let[ting] the model handle the entire building process for you.” However, you’re still ultimately responsible for what’s built. Claude Code may have generated the implementation, but Max set the context, approved permissions, reviewed results, and answered clarifying questions along the way.
3. Build the individual stages of your pipeline. Max wrote a list of prompts to take Claude Code through the process of researching topics (stage 1), scoring them and selecting the most relevant one (stage 2), then generating three hooks and expanding the strongest option into a full script based on the knowledge base and sample content (stage 3). He ran them through Claude Code and ended up with a working three-part pipeline, with each stage triggered manually.
4. Connect the stages into a single workflow you can run with one command. Max had Claude Code combine research, scoring, hook generation, and script writing into a unified content engine that produces a structured JSON file he uses in the next stage. “This is the actual moment that it stops being a few separate prompts and starts becoming an agent,” Max says.
5. Add a browser-based interface. Max built a local dashboard that displays the generated topics, scores, hooks, and scripts. This lets him inspect the results and start new runs from a browser instead of returning to the terminal each time. Here’s a tip from Max on creating a dashboard you’ll actually want to use as your mission control: Be as creative as you want, but “make it look like something designed on purpose, not a default template.”
6. Test the workflow and extend it. The first version Max created had two limitations: It produced scripts only for the top-ranked topic, and starting a new research run still required the terminal. Max solved this by asking Claude Code to generate hooks and scripts for all five topics and add a button that could start a new run from the dashboard. The revised system produced three scripts for each topic, giving him 15 script options per run.

If you’re building something similar, Max recommends starting with a repetitive task you can describe clearly. Build a working version with your preferred coding tool, use it, and then extend it in response to what you learn.

Follow along with Zero to Agent in 30 Minutes on Radar, or watch the latest episode on YouTube, Spotify, Apple, or wherever you get your podcasts. If you’re an O’Reilly member, you can watch live. Save your seat.

Running multiple AI agents on the same project seems like pure chaos, with too many cooks in your development kitchen. But with the GitHub Copilot app, these agents work separately and don’t interfere with each other, allowing you to get more done in less time.

Think of parallel agent sessions like a trip to the laundromat. You can start multiple loads of laundry at the same time, in their own machines. You can set each washer with its own settings, and it won’t impact the other loads. Most importantly, you don’t have to wait for one to finish before you can start another one.

Let’s look at how it works.

Agent sessions, Git worktrees, and context

An agent session is a task you’ve given Copilot, start to finish.

In the GitHub Copilot app, you can manage several sessions through the sessions view. Each card shows its title and how far along the agent is in completing its task.

Each session runs independently of the others. That means you can start a new session whenever you want, and the ones already running won’t be disturbed.

But here’s where the real magic happens. Each agent session in the GitHub Copilot app can run on its own Git worktree. Since each session is isolated, they can run in parallel, all at the same time.

That means you spend your time reviewing and making decisions rather than watching your agents at work.

Plus, each session keeps its own context, so you can switch between them freely. Each picks up exactly where you left it, and you never have to re-explain what you were doing. Less context switching means you feel less scattered.

What this looks like

Let’s look at an example repository, tailspin-toys. There are three things I want to do to this project today: add funded sort, perform an accessibility review, and run some tests on this project.

The first step is to ask Copilot to build the funded sort feature. Just as it gets started, open a new session and ask Copilot to perform an accessibility review. As that gets under way, start prompting Copilot to run some tests in a third session.

In the session view, you can keep track of all of these as they progress and review the results as they finish. Or you can step away entirely, grab a cup of coffee, and know your work is happening without you babysitting it.

Get started

Ready to feel the power of parallel agent sessions?

Try starting two small tasks at the same time in the GitHub Copilot app. It’s a low-stakes way to watch your tasks progress independently.

The post GitHub Copilot app for Beginners: Run several agents at once appeared first on The GitHub Blog.