Thanks to some smart (and aggressive) marketing, Duolingo has cemented itself as one of the most popular language learning apps in the world. That said, it isn't without competition: There are plenty of alternatives to consider, and if you're peeved about Duolingo's plans to phase out its contract workers in favor of using AI, you might be looking into them. There's now a new competitor for Duo's crown, but it certainly isn't a replacement for the AI-averse. It's also far from an underdog, considering the service comes from none other than Google. Yesterday, the company announced a new AI-powered language learning experience it calls Little Language Lessons. It isn't an app, rather an experiment in Google Labs that offers small doses of lessons and immersion for a handful of different languages. Here's how Google's Little Language Lessons currently work.Using Google's Duolingo alternative To start, head to Google Labs' Little Language Lessons site, and sign in with your Google Account. You'll need to agree to a pop-up that warns you that the feature is an early experiment that uses generative AI, and might not always be accurate—a hallmark flaw of AI tools in general.Google is offering 22 language options (including regional dialects for specific languages). Those include:ArabicChinese (China) Chinese (Hong Kong)Chinese (Taiwan)English (AU)English (UK)English (US)French (Canada) French (France)GermanGreekHebrewHindiItalianJapaneseKoreanPortuguese (Brazil)Portuguese (Portugal)RussianSpanish (Latin America)Spanish (Spain)TurkishOnce you're in, there are three different types of lessons to try—or "experiments," as Google calls them. "Experiment no. 001" is "Tiny Lesson," which assists you with words, phrases, and grammar for any situation you can dream up. You choose the language, then type a "purpose or theme" into the provided text field. If you can't think of anything, you can use one of the auto-generated themes Google provides, like "taking a taxi," "scuba diving" or "going on a first date." (Or taking a taxi to scuba dive on your first date.)I went with Portuguese (Portugal) and "taking a taxi." I hit Generate, and Tiny Lesson offered me three categories to work from: Vocabulary, which lists different words I might use ("o táxi" for "the taxi" or "o taxista" for "the taxi driver"); Phrases, such as "Pode chamar um táxi, por favor?" for "Can you call a taxi, please?"; and Tips, like explaining how to use "ter que" to express that I need to go somewhere. Any Portuguese words and phrases have a speaker option to click to hear the proper pronunciation. "Experiment no. 002" is "Slang Hang," which aims to teach you slang and expressions from the regions that speak the language you're learning. For fun, I asked it to run with U.S. English for this one. Slang Hang generates a story between two people, and, following a short premise, generates a conversation between them to demonstrate how they might naturally speak to one another. Expressions and slang are underlined, and, again, you can click the speaker icon to hear them spoken out loud. One side of the conversation is generated at a time, and you press the space bar to move on to the next person's sentence. If you want a new story altogether, you can hit the refresh icon, or click the X to exit entirely. (Slang Hang is currently missing support for Arabic, Greek, Hebrew, Hindi, Italian, Japanese, Korean, Portuguese (Portugal), and Turkish.) Credit: Lifehacker Finally, there's "Experiment no. 003," or "Word Cam." This feature lets you take and share a photo with the web app to learn how to talk about your surroundings. This works best on a smartphone or tablet, since otherwise you'll only have your computer's front-facing camera to work with. Once you grant access to your camera, snap a photo of your surroundings. Word Cam then analyzes the image, highlights specific elements, and labels them with words in the target language. For example, I took a photo of the street corner, and Word Cam labeled the car as "o carro," the grass as "a relva," and the bush as "arbusto." You can tap each word to pull up a full page about the word in question, as well as examples of how you might use it in different sentences and scenarios.Can Little Language Lessons compete with Duolingo?Little Language Lessons is a neat idea, and I'm looking forward to experimenting with it further. Will this take a bite out of Duolingo? I doubt it. But it does offer a more casual and personal approach to language learning, as opposed to Duolingo's more structured lessons. I like that I can choose what I want to learn more about, especially as, in my view, language acquisition is most effective when you're learning words and phrases you actively use on a daily basis. The main concern is learning the wrong words, which could be an issue if the AI decides to hallucinate an incorrect translation. That's where human intervention would come in handy: If lessons were generated by people and fact-checkers, you'd feel a bit more at ease with accepting the information at face value. With these lessons, however, I feel I have to double-check everything it tells me.

One day after Meta rolled out its standalone AI app, Mark Zuckerberg has shared more about how the company plans to eventually monetize its generative AI assistant. During the company’s first quarter earnings call, Zuckerberg said Meta AI could one day show ads and product recommendations. He also hinted at plans for a subscription component for those who want a more “premium” version of the assistant. "I think that there will be a large opportunity to show product recommendations or ads, as well as a premium service for people who want to unlock more compute for additional functionality or intelligence,” Zuckerberg said. He added that for now the company is more focused on growing Meta AI’s usage. (He announced yesterday that Meta Ai had reached “almost” 1 billion monthly users.) “I expect that we're going to be largely focused on scaling and deepening engagement for at least the next year before we'll really be ready to start building out the business here,” he said. Zuckerberg’s comments — just one day after Meta introduced its standalone AI app — underscores how important the assistant is to the company. The Facebook founder has repeatedly said he wants Meta AI to be the most used AI assistant in the world, and he said on Wednesday’s call that a standalone app would be particularly important for attracting US users. Meta’s strategy for monetizing the assistant in many ways mirrors its approach to Threads, which only just began expanding its early experiments with ads this month long after it reached hundreds of millions of users. Speaking of Threads, Zuckerberg also shared some new milestones for Threads, saying that text-based app now has 350 million monthly active users and that time spent on the platform has increased 35 percent over the last six months thanks to improvements to the company’s recommendations systems. Later in the call, Meta’s CFO Susan Li shared that the company has also been testing its Llama model to power Threads’ recommendations and that the addition of the large language model has led to a 4 percent increase in time spent. “It remains early here, but a big focus this year will be on exploring how we can deploy this for other content types, including photos and videos,” she said.This article originally appeared on Engadget at https://www.engadget.com/social-media/meta-is-a-mulling-ads-and-a-premium-version-of-its-ai-assistant-mark-zuckerberg-says-225202560.html?src=rss

As LG finally shuts down the update servers for its phones in June, we look back at our favorite models from its mobile history.

The company's Azure cloud growth exceeded Wall Street consensus.

The Fast Company Impact Council is an invitation-only membership community of leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual dues for access to peer learning, thought leadership opportunities, events and more. Every week, I talk to software agency founders who are burned out on routine. They’ve mastered the frameworks. They’ve scaled their teams. But what they’re really searching for—often quietly, sometimes urgently—is purpose.  And then something happens. I show them a project where their skills can help thousands of people access healthcare, education, or safety. Their posture changes. The questions sharpen. “Wait, we can actually do that?” Yes. By doing what they already do best—ideate, build, solve—but on a problem that improves lives and even saves them.  That moment is electric.  The term that doesn’t match the work​?​  And yet, after more than a thousand tech-for-good matches—including over 100 AI-driven collaborations—I keep returning to one thing that still doesn’t feel solved: a term.  Pro bono.  It’s the term most often used to describe this work. But in tech, it rarely sparks that same excitement. It sounds like a gesture. A side project. Something small.  That’s not the kind of work we’re seeing.  At Tech To The Rescue, we facilitate projects where software teams build AI tools that process multilingual crisis data in real time to support emergency response; create AI chatbots to combat malnutrition in rural Ecuador; develop early-warning systems in conflict zones; or deploy tools that accelerate child abuse prevention or disease early detection. These aren’t feel-good sprints. They’re high accountability, impact-critical builds—solving problems that are urgent, complex, and impossible to address with off-the-shelf solutions.  From courtrooms to code: The pro bono paradox  In the legal world, pro bono is institutionalized and respected. In tech, it’s fuzzy. There’s no standard or incentive. Too often, it’s misunderstood as junior level or one-off.  We’re not ready to throw the words out. But we are challenging it.  In our world, “pro” already stands for professional. These are scoped, outcome-driven, expert-level projects. When we say pro bono, we mean fully committed tech partnerships—not side gigs. It’s time to reclaim the words.  We call this “extreme matching.” We don’t pair teams with nice ideas—we match them with necessary ones. This isn’t volunteering. It’s strategic problem solving.  The collaboration gap: When technology isn’t the problem  At our recent AI for Health Matching Day, we brought together experts across sectors. Professor Angela Aristidou at Stanford’s Institute for Human-Centered AI and UCL School of Management said it plainly: “The gap is not tech—it’s collaboration.”  It echoed something I hear often. Tech leaders often say, “We’d help—if someone asked, and if we actually knew how.” Nonprofits say: “We didn’t think a company like that would take our call”—or admit they don’t know how to start.  At the same event, Radhika Batra, MD, founder of Every Infant Matters, showed how AI diagnostics and mental health tools are saving lives—but only through deep partnerships. Her organization has helped over 700,000 children avoid blindness. Norberto de Andrade, founder of Polipro.AI and Meta’s former AI policy director, emphasized cross-sector collaboration, experimentation, and prototyping legislation as essential tools in designing a more humane and sensible system for us all.  These aren’t just technology problems. They’re narrative and systems problems. And the way we talk about this work shapes how seriously it’s taken.  Beyond charity: The terminology trap limiting tech’s social impact  In tech, language becomes culture: Agile. Open Source. DevOps. What we call something affects who shows up, how it’s funded, and what gets prioritized.  Just like “vibe coding”—a buzzy term for playful AI experimentation—is trending on social media, maybe “impact coding” or “purpose coding” can describe something more vital: human-centered, real-world problem solving. Maybe it’s something we haven’t named yet—but urgently need to.  What matters is that we start naming and understanding the work in ways that reflect its scale and transformative potential.  From Google.org’s fellowship program to Salesforce’s 1-1-1 model​     ​, tech giants are implementing structured corporate giving frameworks. Meanwhile, smaller agencies and startups struggle to find similar models that fit their scale. Yet our internal data reveals something surprising: SMEs often commit proportionally more time, resources, and consistency to pro bono collaborations than larger companies do. It’s a counterintuitive finding that challenges conventional wisdom about who drives the most meaningful impact. We now need the language, recognition, and infrastructure to match.  Talent wants alignment  At the same time, this momentum is being fueled by a new wave of talent demanding greater alignment between their work and their values. According to Randstad’s 2025 Workmonitor report, which surveyed over 26,000 workers across 35 markets globally, 29% have already quit a job because they didn’t agree with their leaders’ viewpoints or stances. Nearly half (48%) said they would not take a job if the company didn’t share their environmental or social values. And 43% have considered quitting because of their company’s stance on political issues.  Pro bono, high-skilled, social impact work is already happening. It’s not small. It’s not random. It’s not charity. These are long-term, mission-critical partnerships that demand rigor and deliver real results.  Whether we keep the term pro bono or evolve it into something new, one thing is clear: The story needs to change—because the impact already has. And the companies that help rewrite it will define what tech-for-good truly means in the decade ahead.  Jacek Siadkowski is the CEO and cofounder of Tech To The Rescue. 

To the person with a hammer, everything looks like a nail. In outdoor gear manufacturer Nite Ize's case, carabiners are their hammer.Between battery-powered car key fobs and biometric locks, you'd think traditional keys would be going into decline. But Nite Ize reckons that a subset of people—property and facilities managers, or even heads of large households—not only carry a lot of keys, but often need to detach them to share with staff or family. They thus designed this KeyRack Steel S-Biner, inspired by climbing gear. It allows the user to separate keys on their own mini-carabiner and quickly detach them. A "Bigfoot" variant provides five separate "toes" for individual mini-carabiners. They run $11 and $12, respectively.

You know how audiophiles get their own specialist gear? Music equipment specifically tuned to the needs of hi-fidelity audio? Gamers get their own specialist gear too. Low-latency audio solutions, with mics, controls, the works. What about podcast listeners? Can you think of how audio gear would look if it were specifically designed for people who listen to podcasts? That’s what the POD Pods are supposed to be apparently. Designed as a pair of TWS earbuds tailor-made for podcast listeners, the POD Pods come with a few features that truly uplift the podcasting experience. Unlike regular earbuds that just come with a case, the POD Pods pack controls and even a display that’s designed just for podcast-listening, providing everything from a timeline, to AI highlights, and even language translation for foreign-language podcasts. Designer: Lee Huang The POD Pods are, first and foremost, earbuds. They come with a truly wireless design, sitting inside a semi-transparent case that feels reminiscent of stuff from Nothing. The case’s clear lid lets you see the earbuds inside, knowing whether they’re there or not. Meanwhile, a screen on the back, and controls on the side help aid the podcast listening experience. Before we talk about the screen, I really sort of fell in love with the controls first. The POD Pods come with 3 controls, placed strategically on the side of the earbuds case. Two of them are volume controls – understandable, given that it’s literally audio gear. The third, however, is a brilliant podcast-app-UI feature that gets translated into a control. A knob on the side lets you instantly skip forwards or backwards by a period of 15 seconds (anyone who’s used Pocket Casts or another podcast app will relate), making it incredibly easy to skip forwards or backwards while listening to a podcast. Music listeners prefer skipping tracks. Podcast listeners prefer micro-adjusting progress while listening. That intuitive understanding led to the addition of this feature, which, with the flick of your thumb, can send you 15 seconds forward or backward while on a pod episode. That said, the screen might be the most impressive addition to the device’s caseback. The screen uses a low-energy electronic ink display, capable of running for weeks on just tiny amounts of energy. This allows the entire device to still have a great battery life despite boasting a near 2-inch display. The display does a variety of things. When playing music, it showcases the album art, but switch to podcasts and you get access to a variety of power-features, from AI transcription to generating highlights based on content. You even get translation, which would help a lot of non-English speakers listen to podcasts or news – an important feature considering the POD Pods were designed by a Japanese designer. To accompany the POD Pods, designer Lee Huang also created a pod-station of sorts. Designed to work as a charging dock for the pods case, the station still allows you to listen to podcasts thanks to its unique design. Most charging docks just handle battery recharging. This one comes with a speaker and controls, allowing your earbuds to now transform into a tiny internet radio of sorts. It’s clever and takes a very novel approach to novel problems. Nobody really designs audio gear specifically for podcasters – which is why this one feels so refreshingly innovative! Too bad it’s just a concept, though. The post Finally, earbuds built for podcast junkies not music snobs first appeared on Yanko Design.

Capturing a legend in a decade of movie poster art.

A US judge says Apple deliberately chose not to comply with an order requiring it to loosen App Store rules—then tried to cover up its disobedience.

The ruling was a stinging defeat for Apple in a long-running antitrust case brought by Epic Games, the maker of Fortnite, on behalf of app developers.