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AI NewsAdobe camera app’s new feature will critique your photos using AI

Adobe camera app’s new feature will critique your photos using AI

4:47 AM IST · July 21, 2026

Adobe camera app’s new feature will critique your photos using AI

Adobe is adding new AI-powered features to its experimental iOS camera app calledProject Indigo, launched last year. The app previously offered pro controls, multi-frame super-resolution, and different capture modes, and is now adding features that will use LLMs (large language models) to critique photos and provide editing suggestions. It’s also adding other AI features, like advanced object removal, depth of field generation, and the ability to add different styles to photos. Marc Levoy, the person heading Adobe’s project, had previously developed Pixel’s camera chops. He said that most generative AI tools provide prompt-based editing and, often, finding the perfect prompt to tweak a photo or get the right result can be a tricky endeavor. That’s why most of the new experimental AI features are buttons that can generate more deterministic outputs. For instance, Google’scamera coach feature for Pixel phones, launched last year, offered more generic framing suggestions. Project Indigo’s features, by comparison, are fairly descriptive and could help you learn some photography tricks, even if you don’t agree with the AI assessment. There are two features in this category. First is the photo critique, which includes a “professional” opinion about framing, lighting, colors, and emotional impact. The second is capture and edit suggestions, which give you tips about reshooting the photo on how you can change framing, exposure, and objects in the viewfinder. For instance, the app told me to remove the hexagonal white object in the frame from a photo I took. A second section tells you how you can use an existing photo to make it better using Adobe Lightroom controls. Photography apps, including Apple Photos, Google Photos, and Adobe Photoshop, have offered object removal for years. But the feature often depends on the user circling or selecting an object by drawing on the screen, which is not perfect every time. Project Indigo’s new feature gives you toggles for things to remove from an image, like people in the background, trash and trash cans, wires and poles, fences, vehicles, and other clutter. You can also describe a custom object to remove. The results of this feature are pretty impressive. The app removed my friend and the object he was holding from the background without creating any strange artifacts. The app also allows you to use AI to create depth of field for a photo to simulate a blurred background. With this new update, Adobe is also experimenting with the style transfer feature, which lets you turn your picture into tones like watercolor, pen and ink, ink line with color wash, monochromatic, and backlit subject. Some styles remind me ofthe early days of the Prisma app. With advanced models, the outputs look refined, but style transfer is not a new or useful feature. Despite Levoy’s criticism of the prompt-based implementation, Project Indigo has its own feature that lets you describe your edit. Users can use it to perform edit flows that are not available in preset tools. But this is also a potential road to slopland. The company is using Google’s Gemini-based Nano Banana for these features, but it’s open to swapping in other models, including its own Adobe Firefly model. These features, bundled under the AI playground tab, are still in a testing phase, and only select users will get access to them. These features might not ever make it to a wider audience, but it is good to see some features that can make people aware of nuances in photography, rather than just creating more slop.

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Gritt exits stealth with $34 million for robots to build solar plants—then, everything else

Gritt exits stealth with $34 million for robots to build solar plants—then, everything else

One of the most important things happening on Earth today is the solar energy build-out. Around the world, companies and countries are racing to deploy solar and batteries to achieve energy independence and limit the effects of climate change. That build-out, though, is running into a labor market challenge, with a limited supply of workers to meet a growing demand for installation. Robots could be an answer, but industrial robots have historically struggled in unstructured environments, at least until now. The latest generation of AI models may have changed that equation. That’s the driving idea behindGritt, a start-up founded by two Carnegie Mellon-trained roboticists, CEO Puneet Puri and CTO Vishal Dugar. The company exited stealth Tuesday morning with a $26 million Series A round of funding led by Obvious Ventures with participation from Union Square Ventures and Active Impact Investment. That brings its total funding to $34 million, following an earlier seed round backed by First Round Capital, Climactic, Congruent Ventures, and VSC Ventures. The startup is building an intelligent system to “help civilization build infrastructure faster,” in Puri’s words. “Our thesis is that if we truly want to speed up construction,” Puri tells TechCrunch, “you need an intelligence which can work in the outdoor, chaotic environments of these construction sites, and it has to be generalizable enough that it can work in these varied environments.” Rather than building its own robots from scratch, Gritt uses off-the-shelf hardware—thus far, rented skidders and robotic arms built by companies like Kawasaki—to build platforms that are controlled by its AI models. The first job its systems handle is unloading large, glass solar panels, carrying them toward the metal frames where they need to be installed, and positioning them on the frames with sub-millimeter accuracy so workers can fasten them. “There are people who used to build rockets that went into space and had infinite budget for the smallest little part, and then there are people who know what it means to get into dirty, dull, and dangerous jobs and scale them like mad,” said Andrew Beebe, the partner at Obvious Ventures who led Gritt’s Series A round. “These guys are in the second camp, and that’s a special kind of entrepreneur that has the technical chops, the AI, and the machine vision skills to make it work.” Gritt has two systems currently deployed in the field, using the data they collect to improve their behavior. Puri says that a typical eight-person crew workers can install 800 panels a day, but the same crew working with Gritt’s systems can install 3,000 to 4,000 panels each day. Now, the company says it is contracted to help install 2.8 gigawatts of solar panels in the next 18 months, and that its customers include three of the top 10 US power construction companies. The company hopes to be operating 48 of its systems within the next six months. TechCrunch spoke to one Gritt customer who declined to be identified for competitive reasons, but who was enthusiastic about the system’s ability to improve his work. He expects it to be easier to work at remote sites where it is difficult to attract workers, and anticipates a reduction in injuries since workers won’t have to repeatedly lift 100-pound panels overhead. Gritt is competing against companies with their own panel-installing robots likeLuminous Robotics,Cosmic, and China’sTrinabot. Those companies are building their own hardware, rather than focusing on off-the-shelf vehicles and arms like Gritt, a difference that could shape who grows faster and with a leaner cost structure as demand grows. Gritt wants to add new manipulation tasks to its system so it can fasten the solar panels, drill posts, and even build the racks they sit on. Longer term, it also wants to move into other common, labor-intensive construction tasks, like tying rebar before concrete is poured over it. What’s enabled the startup to pursue this vision? Mainly, the rise of new AI models, the founders say. “Making a system for one solution was still possible to some extent five years ago, right?” Puri said, but AI is now making that work generalizable — the same underlying pipeline can be reused and improve across tasks. As an example, he noted that training the system to stack cinder blocks took weeks, while a similar demo with rebar tying took just a day using the same software. But training new tasks is just the beginning of Gritt’s vision. The founders believe the suite of sensors and intelligence its systems bring to worksites can do more than install panels; it can boost management and decision-making. For instance, they imagine their system noticing a trench is open while a storm approaches, allowing it to alert workers to cover it before rain damages components, or flagging missing inventory. “Gritt becomes now this layer of physical AI, which is doing this dextrous, labor-intensive task, plus it can help you take decisions on the site,” Puri said.

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