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Microsoft has been steadily improving its Paint app for Windows 11 in recent years with a dark mode, transparency and layers, and even AI-powered image creation. Now, Microsoft is adding two new Photoshop-like features to Paint.

You’ll soon be able to save your Paint creations as a project file, much like a Photoshop Document (.PSD). The Paint project files are currently being tested with Windows Insiders in the Dev and Canary Channels. “You can now save your creation as an editable Paint project file and seamlessly pick up where you left off,” explains Dave Grochocki, principal product manager lead for Microsoft’s Windows inbox apps.

Paint files will be stored with, you guessed it, a .paint file extension. Paint files will store your layers inside the file, too. “When you’re ready to return, simply open the file to launch it in Paint, right where you left off,” says Grochocki.

Microsoft is also adding opacity sliders to adjust the transparency of the pencil and brush tools in Paint. “To get started, select either tool and use the slider on the left side of the canvas to adjust the opacity to your desired level,” says Grochocki.

If you’re a fan of the Snipping Tool in Windows 11, Microsoft is also adding a quick markup feature that makes it easier to annotate screenshots. There’s a highlighter, pen, and eraser tool in the markup toolbar, as well as the ability to easily re-crop an image.

Finally, Microsoft is also adding AI-powered write, summarization, and rewrite as a free feature inside Notepad for Copilot Plus PC users. You won’t need a Microsoft 365 subscription to use this, but Grochocki says you can “seamlessly switch between local and cloud models based on your needs.” Microsoft is using the local models on Copilot Plus PCs to power this Notepad feature.

Many of the conversations around data center sustainability seem to inevitably boil down to the usual suspects: servers, cooling and power. Yes, these are important areas, and great strides have been made in optimizing those aspects of data center architecture.

But there’s a less obvious contributor to sustainability that’s often overlooked – fiber infrastructure – and when thoughtfully designed, operators can anticipate future needs, extend product lifecycles and reduce waste. As AI and other high-performance computing applications fuel unprecedented demand for data centers, fiber infrastructure can play a critical role in advancing sustainability.

Understanding the Scope of the Challenge

Google’s 10th annual Environmental Report underscores challenges the data center industry faces amid the AI boom. Its emissions swelled by 51% from 2019 to 2024 and grew 11% last year alone, despite prioritizing sustainable practices and pledging to achieve net zero emissions by 2030.

Data centers have historically focused on reducing Scope 1 and 2 emissions, reducing their direct emissions and emissions from purchased energy.  But Scope 3, which includes indirect emissions from the company’s full value chain, represents the largest amount of greenhouse gas emissions for data center operators. In Google’s case, Scope 3 emissions make up 73% of its carbon footprint.

Related:FCC Rules Support More Subsea Cables With Less Foreign Investment

One of the intriguing aspects of fiber infrastructure from a sustainability perspective is that, if designed thoughtfully, it can be used to tackle both Scope 1 and Scope 3 emissions, through reduction in power and cooling use, and in reducing waste and required components.

Fiber for the Future

Thoughtfully designed fiber infrastructure solutions should be able to accommodate future generations of equipment, eliminating the need to replace the system as equipment ages out every five years or so. The trick is staying within the optical loss budget (the amount of light that can be lost in a cable). With bandwidth constantly growing, the optical loss budget drops correspondingly.

For example, optical loss budgets have consistently decreased as speeds have increased from 10 gigabits to 40 gigabits, then 100 gigabits, 200 gigabits, and 400 gigabits. Projecting this trend further out, as the industry moves toward 1.6 terabits per second, the amount of light that can be lost from transmitter to receiver will only continue to decline.

Over the years, manufacturers have made great strides in perfecting the performance of the cable itself. Today, one of the key culprits of optical loss is the traditional cassette-based fiber connectivity solution most data centers employ. Having multiple connection points in the cassette and at its interfaces leads to cumulative signal degradation. Each additional connection, along with internal fiber paths, introduces a small loss, reducing optical headroom. The answer? Reducing the need for additional connections with Alignment Independent Multifiber (AIM) cabling solutions.

AIM cabling enables a direct connection between connectors and two-fiber duplex MDC patch cords via a conversion adapter panel, delivering near-lossless performance, maximizing optical headroom and significantly improving density. The minuscule optical loss is thanks to AIM cabling’s ability to minimize or even eliminate the need for splicing.

By significantly minimizing signal degradation and reducing the space plastic cassettes take up, this shift provides critical enhancements in performance, efficiency and density, forging a durable physical layer that can sustain the intensive, next-generation workloads of AI and high-performance computing – much more sustainably.

Use Less, Waste Less

Beyond optical loss, direct mating breakout connections have an immediate impact on waste because they eliminate the cassette. This translates to a direct reduction in plastic waste. In a trunk and cassette-based system, there is significant plastic involved in the cassettes themselves and additional connectors. With a direct mating breakout connection, the large plastic cassette is replaced by a much smaller adapter plate, using a fraction of the plastic.

Direct mating breakout connections can be further enhanced with extended distance solutions, which, in some cases, can reduce the need for additional signal-amplifying equipment, further decreasing Scope 3 emissions by eliminating the need for this equipment.

Very Small Form Factor (VSFF) transceivers can also support sustainability in terms of space and power consumption. If more fiber can be fitted into a smaller space (e.g., 192 fibers in a rack unit instead of 96), fewer racks are needed, reducing the data center’s physical footprint. More importantly, VSFF transceivers have the potential to reduce the number of chassis required. For example, a chassis that can support 40 gigabit QSFP transceivers at 10 gigabits per lane can be broken out with VSFF connectors.

This one transceiver can take the place of four 10 gigabit SFP transceivers, reducing the number of chassis needed. If each chassis has a certain power draw, reducing the number of chassis leads to a reduction in overall power consumption. Less power consumed means fewer BTUs generated, which in turn reduces cooling requirements.

Look Past the Obvious

The “usual suspects” are usual for a reason. Power and cooling are critical in making up ground in the race toward a more sustainable future in the data center industry. But it’s crucial to look beyond the obvious for further emissions reductions, particularly as AI and HPC applications drive unprecedented demand. Fiber infrastructure, when thoughtfully designed and implemented, presents a significant – and perhaps unexpected – opportunity.

The founders who previously sold their livestreaming video startup Periscope to Twitter are back with a new startup — and no surprise, it’s an AI-focused company this time around.

On Wednesday, former Twitter head of product Kayvon Beykpour announced the launch of Macroscope, an AI system aimed at developers and product leaders that summarizes updates to a codebase and catches bugs, among other things.

The startup was co-founded by Beykpour, now Macroscope CEO, in July 2023, along with childhood friend Joe Bernstein, also previously of Periscope and their prior enterprise startup, Terriblyclever, which was sold to Blackboard in 2009. They’re joined by co-founder Rob Bishop, who sold his computer vision and machine learning company, Magic Pony Technology, to Twitter in 2016.

The company describes its product as an “AI-powered understanding engine” that’s designed to save engineers time, and the type of product the founders “wish we’d had” when building their earlier companies.

Today, engineers use a variety of tools to keep track of work, like JIRA, Linear, and spreadsheets, and spend too much time in meetings instead of building, Beykpour says. Macroscope is designed to fix this.

“I feel like I lived this pain…at every company I worked at, whether it was the startups that we built ourselves, or whether it was enormous public companies like Twitter, we sort of lived this problem the hard way,” Beykpour told TechCrunch in an interview.

“Trying to get a sense for what everyone was doing, especially when you have an organization like Twitter with thousands of engineers, it was literally most of my job — and my least favorite part of my job as the head of product at Twitter,” he said.

To address this issue and others, Macroscope’s customers first install its GitHub app, which gives the company access to the code base. They can then optionally install other integrations, like a Slack app, Linear app, and JIRA app. The software then does the rest of the work by analyzing the code and noting what’s changing.

This involves a process called code walking, which uses the Abstract Syntax Tree (AST) — a structural representation of programming code — to gather important context about how the customer’s code base works. That knowledge is then used in conjunction with large language models (LLMs).

Once up and running, engineers can use Macroscope to discover bugs to fix in their PRs (pull requests), summarize their PRs, get a summary of how the codebase is changing, and ask code research-based questions. Meanwhile, product leaders could use the software to get real-time summaries of product updates, productivity insights, answers to natural language questions about the product, code, or development activity, and more. This can help them determine what teams are prioritizing in terms of engineering allocation.

“You can ask natural language questions, regardless of what your technical ability is,” notes Beykpour. “This might be very useful if you’re trying to learn about the code base without distracting a senior engineer on your team. Very valuable. If you’re a CEO and you want to understand literally, ‘what did we get done this week?’, your options are either ask Macroscope or go distract some teammates,” he adds. “One is a lot more expensive than the other.”

While there isn’t a product that offers a direct competitor to all that Macroscope offers, it does compete in the code review space — where developers examine and test code changes before they’re implemented — with tools like CodeRabbit, Cursor Bugbot, Graphite Diamond, Greptile, and others. However, the company said when it ran its own internal benchmark of over 100 real-world bugs, its product caught 5% more bugs than the next-best tool. It also generated 75% fewer comments. (It shared its benchmark publicly in a blog post.)

The software costs $30 per active developer per month, starting at five seats, and offers enterprise pricing and custom integrations for larger businesses. It requires the use of GitHub Cloud. Ahead of its launch, a number of startups and larger firms have been using the product, including XMTP, Things, United Masters, Bilt, Class.com, Seed.com, ParkHub, A24 Labs, and others.

The San Francisco-based startup has a team of 20 and is backed by $30 million in Series A funding, which was closed in July and led by Michael Mignano at Lightspeed. Other investors include Adverb, Thrive Capital, and Google Ventures. To date, Macroscope has raised $40 million total.