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Ride-hail and delivery giant Uber is introducing cheap, fixed-route rides along busy corridors during weekday commute hours in major U.S. cities – one solution to a world that feels, for most people, more expensive everyday. Starting Wednesday, riders in Baltimore, Boston, Chicago, Dallas, New York City, Philadelphia, and San Francisco will be able to save 50% off the price of an UberX trip by booking with Uber’s new “Route Share” feature. The company announced Route Share and other new features and discounts designed to help customers save money on rides and deliveries at its annual Go-Get event. The aim is to attract and maintain a loyal customer base that continues to use the Uber app in spite of outside economic pressures. The commuter shuttles will drive between pre-set stops every 20 minutes, according to Sachin Kansal, Uber’s chief product officer. He noted that there will be dozens of routes in each launch city – like between Williamsburg and Midtown in NYC. The routes, which are selected based on Uber’s extensive data on popular travel patterns, might have one or two additional stops to pick up other passengers. To start, riders will only ever have to share the route with up to two other co-riders. Riders can book a seat anywhere from seven days to 10 minutes before a scheduled pickup, and the app will provide them with turn-by-turn directions to get them from their house to the corner where they’ll be picked up. Uber is relying on the same underlying technology that it uses for Uber Share, its shared rides offering where riders can get 15% to 30% off the cost of an UberX ride by pooling with others. Kansal told TechCrunch that Uber completes millions of shared trips annually and has been seeing more traction lately as riders look for more ways to save. Hence, Route Share. “Because of the size of our network, both on the consumer side as well as the driver side, and our core matching and market-based technology, it allows us to do something like this and put multiple people in the same car while creating efficiency and predictability for their commute,” Kansal told TechCrunch. Techcrunch event Join us at TechCrunch Sessions: AI Secure your spot for our leading AI industry event with speakers from OpenAI, Anthropic, and Cohere. For a limited time, tickets are just $292 for an entire day of expert talks, workshops, and potent networking. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW Uber envisions a future where Route Share could qualify for pre-tax commuter benefits. However, as a spokesperson noted, the company would need to find a way to match those trips with Uber XL vehicles. That’s because only six-seater vehicles would meet the eligibility requirements. A potential progression of Route Share would involve autonomous vehicles, particularly in chaotic cities like New York City where no self-driving car companies have deigned to test in. Uber has partnerships with 18 AV companies and during its first-quarter earnings delivery last week, the company reported it has grown to an annualized rate of 1.5 million mobility and delivery AV trips on the Uber network. One of Uber’s more recent AV partners is with Volkswagen. The two plan to work together to add autonomous versions of VW’s ID. Buzz AD electric vehicles to the Uber app – specifically for shared rides – starting in Los Angeles in 2026. “You can see a natural extension of us being able to bring Route Share to autonomous vehicles, as well,” Kansal said. “[Route Share] has a lot of advantages for the autonomous vehicle. It’s a very well-defined route, and so the pickups and drop offs are predictable.” Other ways frequent Uber users can save Image Credits:Uber Each of Uber’s Get-Go events has a theme. Last year, it was focused on ways to help people spend more time together. This year, with economic uncertainty looming due to President Trump’s tariffs, mass layoffs across the tech world, and AI coming for all our jobs, Uber is focusing on cost savings for riders. In the process, Uber hopes to create a predictable cash flow and stickiness that keeps riders engaged with Uber. “Of late, what we have heard very loud and clear is people feel very uncertain, people feel overwhelmed, and people are feeling the prices in many different walks of life, and there’s this need and desire to get more affordable options,” Kansal said. “So everything we’re announcing is squarely focused on how we make life more affordable for them.” One of Uber’s new features is “ride passes” which riders can use to “protect their price for a 1-hour window each day on their selected routes.” There’s two ways this will work. Either riders can pay $2.99 to lock-in a price on a specific route, or they can pay up-front and buy a bundle of prepaid trips. They can buy five, 10, 15, or 20 rides for an “even deeper discount.” The price lock offer will be available for riders in Chicago, Dallas, Houston, Las Vegas, Miami, Nashville, Orlando, Phoenix, San Francisco, and Washington D.C. starting Wednesday, with the rest of the U.S. and Brazil to follow. In the fall, price lock and prepaid passes will be available for teen accounts, too. On the Uber Eats side of things, Uber is going deeper into its partnership with OpenTable and launching a feature called Dine Out, which lets customers in the U.S., Canada, Mexico, the U.K., Ireland, and Australia book tables via OpenTable on the Uber app. When they reserve (either on the Uber app or on OpenTable’s app), they’ll get a discount on an Uber ride to the restaurant. Additionally, OpenTable members will soon be able to use their points on Uber and Uber Eats – not unlike Uber’s partnership with Delta Air Lines. These kinds of deals could offer savings for riders, particularly during peak demand times when surge pricing is in effect. But they’re likely more beneficial for people who frequently use Uber’s service. Prepaid packages often feel cheaper due to upfront discounts, but riders could also overestimate how much they’ll use them. Uber’s pricing strategy is also not transparent, and some reports have suggested that riders with prepaid credits or gift cards get higher fare quotes compared to those paying per ride. (Anecdotally, whenever I switch from my payment method from my personal card to my business card, the price of the ride jumps a few dollars.) An Uber spokesperson told TechCrunch the price lock feature is based on historical prices of that trip, and the prepaid pass protects against price spikes and adds discounts that are also based on historical prices.

TikTok announced on Wednesday that it’s expanding its suite of accessibility tools by rolling out AI-generated ALT text for photos, a high contrast mode, and support for device-level bold text preferences. TikTok introduced support for ALT text for photos a few weeks ago, allowing creators to provide descriptive text for photos to help users with screen readers understand the visual content through spoken descriptions. Now, the company is testing AI-generated ALT text to automatically describe photos out loud when creators don’t add alt text manually. The company says AI-generated ALT text will make TikTok more accessible for people with visual impairments. The move comes as Instagram has been using AI for ALT text since 2018. Image Credits:TikTok As for the new high contrast mode, users will be able to switch it on with the new “color contrast” option in the app’s accessibility section. Doing so will increase the foreground color of text, icons, and user interface elements. This change will make TikTok more usable for people with low vision or sensitivity to low contrast, the company says. TikTok now also adheres to a user’s device’s bold text setting, which means that it will automatically display all in-app text in bold when enabled. This enhances readability for users who prefer or rely on bolder fonts. “We know that when people feel seen, heard, and supported, they’re empowered to share their stories, create and connect with others in meaningful ways,” the company said in a blog post. “That’s why we’re focused on continuously building products that serve everyone.” We see accessibility not just as a responsibility, but as an opportunity to innovate and foster a more inclusive platform for our global community.” The new features join TikTok’s existing suite of accessibility tools, which includes features like auto-generated captions for video content, dark mode, text-to-speech, and the ability to adjust text size across the app. Techcrunch event Join us at TechCrunch Sessions: AI Secure your spot for our leading AI industry event with speakers from OpenAI, Anthropic, and Cohere. For a limited time, tickets are just $292 for an entire day of expert talks, workshops, and potent networking. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW

Google’s AI R&D lab, DeepMind says it has developed a new AI system to tackle problems with “machine-gradeable” solutions. In experiments, the system, called AlphaEvolve, could help optimize some of the infrastructure Google uses to train its AI models, DeepMind said. The company says it’s building a user interface for interacting with AlphaEvolve, and plans to launch an early access program for selected academics ahead of a possible broader rollout. Most AI models hallucinate. Owing to their probabilistic architectures, they confidently make things up sometimes. In fact, newer AI models like OpenAI’s o3 hallucinate more than their predecessors, illustrating the challenging nature of the issue. AlphaEvolve introduces a clever mechanism to cut down on hallucinations: an automatic evaluation system. The system uses models to generate, critique and arrive at a pool of possible answers to a question, and automatically evaluates and scores the answers for accuracy. DeepMind’s AlphaEvolve system is designed to be used by domain experts, the lab says.Image Credits:DeepMind AlphaEvolve isn’t the first system to take this tack. Researchers, including a team at DeepMind several years ago, have applied similar techniques in various math domains. But DeepMind claims AlphaEvolve’s use of “state-of-the-art” models — specifically Gemini models — makes it significantly more capable than earlier instances of AI. To use AlphaEvolve, users must prompt the system with a problem, optionally including details like instructions, equations, code snippets and relevant literature. They must also provide a mechanism for automatically assessing the system’s answers in the form of a formula. Because AlphaEvolve can only solve problems that it can self-evaluate, the system can only work with certain types of problems — specifically those in fields like computer science and system optimization. In another major limitation, AlphaEvolve can only describe solutions as algorithms, making it a poor fit for problems that aren’t numerical. Techcrunch event Join us at TechCrunch Sessions: AI Secure your spot for our leading AI industry event with speakers from OpenAI, Anthropic, and Cohere. For a limited time, tickets are just $292 for an entire day of expert talks, workshops, and potent networking. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW To benchmark AlphaEvolve, DeepMind had the system attempt a curated set of around 50 math problems spanning branches from geometry to combinatorics. AlphaEvolve managed to “rediscover” the best-known answers to the problems 75% of the time and uncover improved solutions in 20% of cases, claims DeepMind. DeepMind also evaluated AlphaEvolve on practical problems, like boosting the efficiency of Google’s data centers, and speeding up model training runs. According to the lab, AlphaEvolve generated an algorithm that continuously recovers 0.7% of Google’s worldwide compute resources on average. The system also suggested an optimization that reduced the overall time it takes Google to train its Gemini models by 1%. To be clear, AlphaEvolve isn’t making breakthrough discoveries. In one experiment, the system was able to find an improvement for Google’s TPU AI accelerator chip design that had been flagged by other tools earlier. DeepMind, however, is making the same case that many AI labs do for their systems: that AlphaEvolve can save time while freeing up experts to focus on other, more important work.

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Gemini, Google’s AI-powered chatbot, can now connect to GitHub — for users subscribed to the $20-per-month Gemini Advanced plan, that is. As of Wednesday, Gemini Advanced customers can directly add a public or private codebase on GitHub to Gemini to allow the chatbot to generate and explain code, debug existing code, and more. Users can connect GitHub to Gemini by clicking the “+” button in the prompt bar, selecting “import code,” and pasting a GitHub URL. Gemini Advanced now connects with @github, making it a more powerful coding assistant. Directly connect to public or private GitHub repos to generate/modify functions, explain complex code, ask questions about your codebase, debug and more. Click the + button in the prompt bar,… pic.twitter.com/EgItxk6hd7 — Google Gemini App (@GeminiApp) May 14, 2025 A word of warning: AI models, including Google’s, still struggle to code quality software. Code-generating AI tends to introduce security vulnerabilities and errors, owing to weaknesses in areas like the ability to understand programming logic. One recent evaluation of Devin, a popular AI coding tool, found that it could only complete three out of 20 programming tests. The new GitHub integration arrives just a few days after OpenAI launched a GitHub connector for ChatGPT deep research, a tool that searches across the web and other sources to compile thorough research reports on a topic. AI companies, including Google and OpenAI, are moving quickly to ship new capabilities in an effort to make their products stand out in an increasingly crowded field. Case in point, OpenAI on Tuesday brought SharePoint and Microsoft OneDrive connectors to ChatGPT deep research.

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AI-powered note-taking tool Granola has been on a roll. The startup’s seen a steep uptick in usage since it launched a year ago, mostly thanks to word of mouth among VCs and founders, but a big driver seems to be the fact that people are using it for doing more than its core pitch — automated note-taking for meetings. Granola’s co-founder, Chris Pedragel, told TechCrunch that the company’s users are increasingly using Granola for taking personal notes, which helps them make all their information, both from work and otherwise, available to the app’s AI to parse and surface insights from. “[People] have Granola open all day because they have a lot of meetings, so it’s like […] where they’re starting to live,” he said. Pedragel said Granola’s organic popularity among the tech crowd and diversifying use-cases has helped its user base grow 10% every week since its launch, though he didn’t specify how many users it currently has. Off the back of that rapid growth and popularity, Granola on Wednesday said it has raised $43 million in a Series B funding round led by Nat Friedman and Daniel Gross’s venture firm, NFDG, at a valuation of $250 million. The round also saw participation from existing investors Lightspeed and Spark, as well as angel investors including Vercel’s Guillermo Rauch, Replit’s Amjad Masad, Shopify’s Tobi Lutke, and Linear’s Karri Saarinen. The round brings the company’s total funding raised to $67 million. Image Credits: Granola Alongside this funding, Granola is also extending its remit beyond its current single-user focus to make itself more useful for businesses: It’s launching a new collaboration feature that lets users share transcripts and notes with teammates, and enable the app’s AI take to advantage of a broader pool of notes and details to surface insights. Users in an organization can create custom folders for various collaborative use cases like sales calls, customer feedback and hiring. The app will also let users share meeting notes with people who don’t use Granola to let them chat with its AI and ask it questions. Techcrunch event Join us at TechCrunch Sessions: AI Secure your spot for our leading AI industry event with speakers from OpenAI, Anthropic, and Cohere. For a limited time, tickets are just $292 for an entire day of expert talks, workshops, and potent networking. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW Other meeting transcription and note-taking apps, such as Read AI, Fireflies and Otter, already offer similar shared space features. Pedregal, though, says Granola is for more than note-taking. “I think how Granola differs from other notetakers is that it is very personal and you are in control all the time. You can edit notes at any point. It is not about just capturing a meeting, but it is a space where you can work, even post meetings,” he said. Earlier this month, Granola updated its app to enable users to ask the AI bot questions about all meetings it had recorded. Building on that, the company will now allow users to ask questions about specific folders as well. Image Credits: Granola Granola’s new collaborative focus is part of a broader trend — many AI-powered meeting transcription and note-taking tools are expanding their focus and building integrations with other tools as they try to become a hub that stores and lets users search through knowledge from various sources. Meanwhile, productivity suites are introducing transcription tools to keep customers from having to use other apps for that purpose. For instance, Notion just yesterday launched an AI meeting note-taking tool. Lightspeed’s Mike Mignano believes that Granola has an edge in this space because of its interface and user experience. “Since the start, the company has had the right mix of AI transcript and human control of taking notes. Now that they are building context across the meetings and making the notes shareable, the product has become stronger. With these features, Granola will have long-term context for users and teams, kicking off network effects for the startup,” he said.

Enterprises want access to new software and AI tools but can’t risk sending their sensitive data out to a third-party software-as-a-service (SaaS) providers. Tensor9 looks to help software companies land more enterprise customers by helping them deploy their software directly into a customer’s tech stack. Tensor9 converts a software vendor’s code into the format needed to deploy into their customer’s tech environment. Tensor9 then makes a digital twin of the deployed software, or a miniaturized model of the deployed software’s infrastructure, so Tensor9’s customers can monitor how the software is working in their customer’s environment. Tensor9 can help companies deploy into any premise ranging from cloud to bare metal servers. Michael Ten-Pow, Tensor9’s co-founder and CEO, told TechCrunch that Tensor9’s ability to transfer software to any premise, and its use of digital twin technology to help with remote monitoring, helps Tensor9 stand out from other companies, like Octopus Deploy or Nuon, that also help companies deploy software into a customer’s environment. “You can’t just throw a piece of software over the wall, or it’s very difficult to throw a piece of software over the wall, and know what’s going on, be able to find issues, debug them, fix them,” Ten-Pow (pictured above, left) said. “They see it running, they can debug it, they can log in and understand what the issues are and fix them.” He said the timing is right for Tensor9’s tech due to tailwinds from the rise of AI. Enterprises and financial institutions want to adopt AI tech, but can’t risk sending their data to a third-party. “An enterprise search vendor might go to, let’s say, J.P. Morgan and say, ‘hey, I need access to all your six parabytes of data to build an intelligent search layer on top of it so that your internal employees can have a conversation with their company’s data,’ there’s no way that’s going to work,” Ten-Pow said. Ten-Pow, an ex-engineer at AWS, said he had a “long, fairly winding path” to the launch of Tensor9. He got the idea for the company while working on another potential idea that didn’t work out. He spent some time figuring out if he could find a way to make it easier for software vendors to get SOC 2 certified, a cybersecurity compliance framework, to help them unlock customers that required their vendors to have it. Techcrunch event Join us at TechCrunch Sessions: AI Secure your spot for our leading AI industry event with speakers from OpenAI, Anthropic, and Cohere. For a limited time, tickets are just $292 for an entire day of expert talks, workshops, and potent networking. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW While that didn’t work out, he discovered from customer calls that what enterprises really wanted was the software to just run in their own tech environment. But many software companies, especially startups, don’t have the resources to offer a bespoke on-premise option for each enterprise customer. That sentiment became the basis for Tensor9 which Ten-Pow launched in 2024. Later in the year, he brought on two of his ex-AWS colleagues, Matthew Michie and Matthew Shanker, as co-founders. The company found early traction with voice AI companies. Since then, they’ve started to expand to work in other verticals including: enterprise search, enterprise databases and data management. The company currently works with AI companies including: 11x, Retell AI and Dyna AI, among others. Tensor9 bootstrapped for its first year and recently raised a $4 million seed round led by Wing VC with participation from Level Up Ventures, Devang Sachdev of Model Ventures, NVAngels, an angel group of ex-Nvidia employees, and other angel investors. Getting investors on board with the idea wasn’t too challenging, Ten-Pow said, because the VCs they spoke with had seen their portfolio companies struggle with this exact problem. Tensor9 just had to convince investors that they were the right team for the job. “We have a simple model but underneath the covers there’s a lot of complexity that makes that happen, hard technical challenges that we’ve solved to make that happen,” Ten-Pow said. “I think that was one of the things that helped us convince the investors to invest in us.” The company plans to use the funding for hiring and for building out the next generation of its technology so that it can work with customers in more verticals. “There’s been an evolution from [on premise] to the cloud and we think that this idea of software lives where it needs to, and operates where it needs to, is that next step that’s a sort of synthesis of the previous on-premise and cloud ideas,” Ten-Pow said.