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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.

View File AIomatic v2.4.6 - Automatic AI Content Writer AIomatic is a breaking edge AI content writer plugin that is ideal for auto blogging and automatical post publishing or updating. It uses OpenAI’s GPT-3 AI engine to turn your website into a auto blogging or even a money making machine! Submitter ceacer Submitted 05/14/25 Category Wordpress Plugins Null  

View File Ultimate Addons for Contact Form 7 Pro Ultimate Addons for Contact Form 7 Pro is a powerful plugin that extends the functionality of the Contact Form 7 plugin, allowing you to create more advanced and customisable forms. This add-on offers many features, including multi-step forms, conditional logic, additional field types, and enhanced styling options. With its user-friendly interface, you can easily design complex forms that cater to your specific needs without any coding knowledge. Ultimate Addons for Contact Form 7 Pro integrates seamlessly with popular third-party services, improving form performance and user engagement. By using this plugin, you can create professional and highly functional forms that enhance user experience and streamline data collection. Submitter ceacer Submitted 05/14/25 Category Wordpress Plugins Null  

View File HBook v2.1 - Hotel booking system - WordPress Plugin HBook will allow you to easily enable online reservation on your WordPress website. It is perfectly suited for anybody owning a business in the hospitality industry: hotel, B&B, holiday apartment, campsite… Thanks to HBook you will be able to add availability calendars, table rates and booking forms on your website in seconds! Submitter ceacer Submitted 05/14/25 Category Wordpress Plugins Null  

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View File Gravity Flow v2.9.12 Automate your business processes. Turn your email and spreadsheet chaos into secure, scalable and efficient workflow systems. Built for WordPress & Gravity Forms. Submitter ceacer Submitted 05/14/25 Category Wordpress Plugins Null  

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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View File BRW v1.7.9 - Booking Rental Plugin WooCommerce Booking & Rental Plugin WooCommerce – With multiple settings and flexible features of the plugin, it is fit with multiple rental systems. The rental plugin developed from woocommerce, so you can customize features and templates with ease. The rental plugin is applicable for systems like: Car Rental, Hotel Booking, Tour Booking, Travel Booking, Boat Rental, Bicycle, Transportation Service, Taxi (Location) Service … Submitter ceacer Submitted 05/14/25 Category Wordpress Plugins Null  

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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.

Data analytics platform Databricks said on Wednesday that it has agreed to acquire Neon, a startup building an open source alternative to AWS Aurora Postgres, for about $1 billion. Databricks said acquiring Neon’s tech would let it combine the startup’s serverless relational database management system with its own data intelligence services to let its customers deploy AI agents more efficiently. Founded in 2021 by CEO Nikita Shamgunov and software engineers Heikki Linnakangas and Stas Kelvich, Neon offers a managed cloud-based database platform (with free and usage-based paid plans) that lets developers clone databases and preview changes before they go to production. The platform automatically scales processor, memory and storage according to usage, and supports branching — isolated database instances for testing and development — as well as point-in-time recovery. Those capabilities, Databricks says, are ideally suited to workloads run by AI agents, which operate faster than human developers but often require supervision to control for errors. Citing recent telemetry, the company said 80% of the databases “provisioned on Neon were created automatically by AI agents rather than by humans.” “The era of AI-native, agent-driven applications is reshaping what a database must do,” said Ali Ghodsi, co-founder and CEO of Databricks, in a statement. “Neon proves it: four out of every five databases on their platform are spun up by code, not humans. By bringing Neon into Databricks, we’re giving developers a serverless Postgres that can keep up with agentic speed, pay-as-you-go economics and the openness of the Postgres community.” Neon has so far raised $129.5 million, according to Crunchbase, and its investors include Microsoft’s venture arm M12, General Catalyst, Menlo Ventures, and Notable Capital. Databricks, for its part, has so far accumulated more than $19 billion in financing, and in January closed a $15.3 billion financing at a $62 billion valuation. Databricks hasn’t held back from dipping into its warchest as it seeks to capitalize on the AI boom and position itself as a top service to build, test and deploy AI models and agents. The company last June acquired data management company Tabular, reportedly for nearly $2 billion, and in 2023 bought MosaicML, an open-source platform for training large language models and deploying AI tools, for $1.3 billion. 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