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ChatGPT is now a partner for your most ambitious work

ChatGPT Work is an agent that can take action across your apps and files, stay with a project for hours if needed, and turn a goal into finished work.

Best Universities To Study AI in 2026

Artificial intelligence has made enormous strides in the past few years – with the introduction of a wide range of AI tools changing the landscape of how we assess data and operate within online spaces forever. This page ranks the 50 best universities to study AI around the world, based on scope, prestige, and the level of AI-related research each institution has released. Career prospects in AI There is a huge demand for individuals with a high degree of skills in artificial intelligence and machine learning, making AI a potential lucrative career prospect with countless opportunities as AI continues to The post Best Universities To Study AI in 2026 appeared first on DailyAI.

Our approach to government and national security partnerships

Learn how OpenAI approaches government and national security partnerships, with principles for responsible AI use, democratic accountability, and public safety.

Sen. Tim Scott wants to hear from Warsh on data centers and artificial intelligence - CNBC

Sen. Tim Scott wants to hear from Warsh on data centers and artificial intelligence CNBC

Google Images gets a Pinterest-like redesign focused on discovery

Now, when users navigate to Google Images, they'll see a "For You" gallery of images tailored to their interests and browsing history.

Best Artificial Intelligence (AI) Stock: AMD, Nvidia, or Broadcom - The Motley Fool

Best Artificial Intelligence (AI) Stock: AMD, Nvidia, or Broadcom The Motley Fool

What Marx Can Tell Us About Artificial Intelligence - Jacobin

What Marx Can Tell Us About Artificial Intelligence Jacobin

The 6 best API integration platforms in 2026

APIs have changed the world for the better. I can check the weather without getting out of bed, or cheat on my diet when the urge for a delivered burrito is just too strong. For consumers like me, the story is pretty clean: an API connects the dots, data transfers, something happens, and life gets a little easier. The enterprise side is a little messier. You probably have dozens of SaaS tools in your tech stack, each with its own data model and authentication quirks. Getting them all to play nic

Claude Code costs up to $200 a month. Goose does the same thing for free.

The artificial intelligence coding revolution comes with a catch: it's expensive. Claude Code, Anthropic's terminal-based AI agent that can write, debug, and deploy code autonomously, has captured the imagination of software developers worldwide. But its pricing — ranging from $20 to $200 per month depending on usage — has sparked a growing rebellion among the very programmers it aims to serve. Now, a free alternative is gaining traction. Goose, an open-source AI agent developed by Block (the financial technology company formerly known as Square), offers nearly identical functionality to Claude Code but runs entirely on a user's local machine. No subscription fees. No cloud dependency. No rate limits that reset every five hours. "Your data stays with you, period," said Parth Sareen, a software engineer who demonstrated the tool during a recent livestream. The comment captures the core appeal: Goose gives developers complete control over their AI-powered workflow, including the ability to work offline — even on an airplane. The project has exploded in popularity. Goose now boasts more than 26,100 stars on GitHub, the code-sharing platform, with 362 contributors and 102 releases since its launch. The latest version, 1.20.1, shipped on January 19, 2026, reflecting a development pace that rivals commercial products. For developers frustrated by Claude Code's pricing structure and usage caps, Goose represents something increasingly rare in the AI industry: a genuinely free, no-strings-attached option for serious work. Anthropic's new rate limits spark a developer revolt To understand why Goose matters, you need to understand the Claude Code pricing controversy. Anthropic, the San Francisco artificial intelligence company founded by former OpenAI executives, offers Claude Code as part of its subscription tiers. The free plan provides no access whatsoever. The Pro plan, at $17 per month with annual billing (or $20 monthly), limits users to just 10 to 40 prompts every five hours — a constraint that serious developers exhaust within minutes of intensive work. The Max plans, at $100 and $200 per month, offer more headroom: 50 to 200 prompts and 200 to 800 prompts respectively, plus access to Anthropic's most powerful model, Claude 4.5 Opus. But even these premium tiers come with restrictions that have inflamed the developer community. In late July, Anthropic announced new weekly rate limits. Under the system, Pro users receive 40 to 80 hours of Sonnet 4 usage per week. Max users at the $200 tier get 240 to 480 hours of Sonnet 4, plus 24 to 40 hours of Opus 4. Nearly five months later, the frustration has not subsided. The problem? Those "hours" are not actual hours. They represent token-based limits that vary wildly depending on codebase size, conversation length, and the complexity of the code being processed. Independent analysis suggests the actual per-session limits translate to roughly 44,000 tokens for Pro users and 220,000 tokens for the $200 Max plan. "It's confusing and vague," one developer wrote in a widely shared analysis. "When they say '24-40 hours of Opus 4,' that doesn't really tell you anything useful about what you're actually getting." The backlash on Reddit and developer forums has been fierce. Some users report hitting their daily limits within 30 minutes of intensive coding. Others have canceled their subscriptions entirely, calling the new restrictions "a joke" and "unusable for real work." Anthropic has defended the changes, stating that the limits affect fewer than five percent of users and target people running Claude Code "continuously in the background, 24/7." But the company has not clarified whether that figure refers to five percent of Max subscribers or five percent of all users — a distinction that matters enormously. How Block built a free AI coding agent that works offline Goose takes a radically different approach to the same problem. Built by Block, the payments company led by Jack Dorsey, Goose is what engineers call an "on-machine AI agent." Unlike Claude Code, which sends your queries to Anthropic's servers for processing, Goose can run entirely on your local computer using open-source language models that you download and control yourself. The project's documentation describes it as going "beyond code suggestions" to "install, execute, edit, and test with any LLM." That last phrase — "any LLM" — is the key differentiator. Goose is model-agnostic by design. You can connect Goose to Anthropic's Claude models if you have API access. You can use OpenAI's GPT-5 or Google's Gemini. You can route it through services like Groq or OpenRouter. Or — and this is where things get interesting — you can run it entirely locally using tools like Ollama, which let you download and execute open-source models on your own hardware. The practical implications are significant. With a local setup, there are no subscription fees, no usage caps, no rate limits, and no concerns about your code being sent to external servers. Your conversations with the AI never leave your machine. "I use Ollama all the time on planes — it's a lot of fun!" Sareen noted during a demonstration, highlighting how local models free developers from the constraints of internet connectivity. What Goose can do that traditional code assistants can't Goose operates as a command-line tool or desktop application that can autonomously perform complex development tasks. It can build entire projects from scratch, write and execute code, debug failures, orchestrate workflows across multiple files, and interact with external APIs — all without constant human oversight. The architecture relies on what the AI industry calls "tool calling" or "function calling" — the ability for a language model to request specific actions from external systems. When you ask Goose to create a new file, run a test suite, or check the status of a GitHub pull request, it doesn't just generate text describing what should happen. It actually executes those operations. This capability depends heavily on the underlying language model. Claude 4 models from Anthropic currently perform best at tool calling, according to the Berkeley Function-Calling Leaderboard, which ranks models on their ability to translate natural language requests into executable code and system commands. But newer open-source models are catching up quickly. Goose's documentation highlights several options with strong tool-calling support: Meta's Llama series, Alibaba's Qwen models, Google's Gemma variants, and DeepSeek's reasoning-focused architectures. The tool also integrates with the Model Context Protocol, or MCP, an emerging standard for connecting AI agents to external services. Through MCP, Goose can access databases, search engines, file systems, and third-party APIs — extending its capabilities far beyond what the base language model provides. Setting Up Goose with a Local Model For developers interested in a completely free, privacy-preserving setup, the process involves three main components: Goose itself, Ollama (a tool for running open-source models locally), and a compatible language model. Step 1: Install Ollama Ollama is an open-source project that dramatically simplifies the process of running large language models on personal hardware. It handles the complex work of downloading, optimizing, and serving models through a simple interface. Download and install Ollama from ollama.com. Once installed, you can pull models with a single command. For coding tasks, Qwen 2.5 offers strong tool-calling support: ollama run qwen2.5 The model downloads automatically and begins running on your machine. Step 2: Install Goose Goose is available as both a desktop application and a command-line interface. The desktop version provides a more visual experience, while the CLI appeals to developers who prefer working entirely in the terminal. Installation instructions vary by operating system but generally involve downloading from Goose's GitHub releases page or using a package manager. Block provides pre-built binaries for macOS (both Intel and Apple Silicon), Windows, and Linux. Step 3: Configure the Connection In Goose Desktop, navigate to Settings, then Configure Provider, and select Ollama. Confirm that the API Host is set to http://localhost:11434 (Ollama's default port) and click Submit. For the command-line version, run goose configure, select "Configure Providers," choose Ollama, and enter the model name when prompted. That's it. Goose is now connected to a language model running entirely on your hardware, ready to execute complex coding tasks without any subscription fees or external dependencies. The RAM, processing power, and trade-offs you should know about The obvious question: what kind of computer do you need? Running large language models locally requires substantially more computational resources than typical software. The key constraint is memory — specifically, RAM on most systems, or VRAM if using a dedicated graphics card for acceleration. Block's documentation suggests that 32 gigabytes of RAM provides "a solid baseline for larger models and outputs." For Mac users, this means the computer's unified memory is the primary bottleneck. For Windows and Linux users with discrete NVIDIA graphics cards, GPU memory (VRAM) matters more for acceleration. But you don't necessarily need expensive hardware to get started. Smaller models with fewer parameters run on much more modest systems. Qwen 2.5, for instance, comes in multiple sizes, and the smaller variants can operate effectively on machines with 16 gigabytes of RAM. "You don't need to run the largest models to get excellent results," Sareen emphasized. The practical recommendation: start with a smaller model to test your workflow, then scale up as needed. For context, Apple's entry-level MacBook Air with 8 gigabytes of RAM would struggle with most capable coding models. But a MacBook Pro with 32 gigabytes — increasingly common among professional developers — handles them comfortably. Why keeping your code off the cloud matters more than ever Goose with a local LLM is not a perfect substitute for Claude Code. The comparison involves real trade-offs that developers should understand. Model Quality: Claude 4.5 Opus, Anthropic's flagship model, remains arguably the most capable AI for software engineering tasks. It excels at understanding complex codebases, following nuanced instructions, and producing high-quality code on the first attempt. Open-source models have improved dramatically, but a gap persists — particularly for the most challenging tasks. One developer who switched to the $200 Claude Code plan described the difference bluntly: "When I say 'make this look modern,' Opus knows what I mean. Other models give me Bootstrap circa 2015." Context Window: Claude Sonnet 4.5, accessible through the API, offers a massive one-million-token context window — enough to load entire large codebases without chunking or context management issues. Most local models are limited to 4,096 or 8,192 tokens by default, though many can be configured for longer contexts at the cost of increased memory usage and slower processing. Speed: Cloud-based services like Claude Code run on dedicated server hardware optimized for AI inference. Local models, running on consumer laptops, typically process requests more slowly. The difference matters for iterative workflows where you're making rapid changes and waiting for AI feedback. Tooling Maturity: Claude Code benefits from Anthropic's dedicated engineering resources. Features like prompt caching (which can reduce costs by up to 90 percent for repeated contexts) and structured outputs are polished and well-documented. Goose, while actively developed with 102 releases to date, relies on community contributions and may lack equivalent refinement in specific areas. How Goose stacks up against Cursor, GitHub Copilot, and the paid AI coding market Goose enters a crowded market of AI coding tools, but occupies a distinctive position. Cursor, a popular AI-enhanced code editor, charges $20 per month for its Pro tier and $200 for Ultra—pricing that mirrors Claude Code's Max plans. Cursor provides approximately 4,500 Sonnet 4 requests per month at the Ultra level, a substantially different allocation model than Claude Code's hourly resets. Cline, Roo Code, and similar open-source projects offer AI coding assistance but with varying levels of autonomy and tool integration. Many focus on code completion rather than the agentic task execution that defines Goose and Claude Code. Amazon's CodeWhisperer, GitHub Copilot, and enterprise offerings from major cloud providers target large organizations with complex procurement processes and dedicated budgets. They are less relevant to individual developers and small teams seeking lightweight, flexible tools. Goose's combination of genuine autonomy, model agnosticism, local operation, and zero cost creates a unique value proposition. The tool is not trying to compete with commercial offerings on polish or model quality. It's competing on freedom — both financial and architectural. The $200-a-month era for AI coding tools may be ending The AI coding tools market is evolving quickly. Open-source models are improving at a pace that continually narrows the gap with proprietary alternatives. Moonshot AI's Kimi K2 and z.ai's GLM 4.5 now benchmark near Claude Sonnet 4 levels — and they're freely available. If this trajectory continues, the quality advantage that justifies Claude Code's premium pricing may erode. Anthropic would then face pressure to compete on features, user experience, and integration rather than raw model capability. For now, developers face a clear choice. Those who need the absolute best model quality, who can afford premium pricing, and who accept usage restrictions may prefer Claude Code. Those who prioritize cost, privacy, offline access, and flexibility have a genuine alternative in Goose. The fact that a $200-per-month commercial product has a zero-dollar open-source competitor with comparable core functionality is itself remarkable. It reflects both the maturation of open-source AI infrastructure and the appetite among developers for tools that respect their autonomy. Goose is not perfect. It requires more technical setup than commercial alternatives. It depends on hardware resources that not every developer possesses. Its model options, while improving rapidly, still trail the best proprietary offerings on complex tasks. But for a growing community of developers, those limitations are acceptable trade-offs for something increasingly rare in the AI landscape: a tool that truly belongs to them. Goose is available for download at github.com/block/goose. Ollama is available at ollama.com. Both projects are free and open source.

9 demos of Gemini Omni and Gemini 3.5 in action

Watch 9 videos showing the capabilities of Gemini Omni and Gemini 3.5, announced at Google I/O 2026.

How sales teams use ChatGPT Work

See how sales teams can use ChatGPT Work to create pipeline briefs, meeting prep packets, forecast reviews, account plans, and stalled-deal diagnoses from real work inputs.

Nobel laureates, experts discuss nuclear war and AI at Borgo Laudato Si - Vatican News

Nobel laureates, experts discuss nuclear war and AI at Borgo Laudato Si Vatican News

As Artificial Intelligence Drives Health Innovations, UN Agencies Launch Joint Strategic Guidelines - Health Policy Watch

As Artificial Intelligence Drives Health Innovations, UN Agencies Launch Joint Strategic Guidelines Health Policy Watch

Our latest Google Finance upgrades, including a new app

The new Google Finance is coming out of beta and launching a new Android app.

Best Artificial Intelligence (AI) Stock: AMD, Nvidia, or Broadcom - Yahoo Finance

Best Artificial Intelligence (AI) Stock: AMD, Nvidia, or Broadcom Yahoo Finance

This one record jump in prices is popping up more in our daily lives. Blame AI. - The Washington Post

This one record jump in prices is popping up more in our daily lives. Blame AI. The Washington Post

How we used Gemini to build Google I/O 2026

Learn how Googlers used AI to produce Google I/O 2026.

June Pixel Drop: New features for creators, Gemini upgrades and more

Get new screen recording feature, text-to-video tools with Gemini Omni, and better multitasking on your Pixel devices.

The real AI race may no longer be at the frontier

Hugging Face CEO Clem Delangue says enterprises increasingly want open models, due to cost, accessibility, and ownership. Do frontier models still matter if most production AI ends up running on open models?

MUFG aims to become AI-native with OpenAI

MUFG uses ChatGPT Enterprise to build an AI-native organization, improve workflows, and deliver new AI-powered financial services at scale.

Satya Nadella has issued a shocking warning to companies using AI

Of all the debates raging about the potential downsides of AI, there is one worry causing the most hand-wringing among AI enthusiasts in Silicon Valley — that the giant AI labs that sell proprietary models are somehow acting like Trojan horses.

The 6 best MuleSoft alternatives in 2026

My uncle bought himself some farmland and found a tractor guy. Not a general mechanic; a rural Einstein who has the knowledge, parts, and patience to service a tractor that predates the metric system. Every time that piece of metal makes a weird noise or just won't work right, it's off to the guy, where it will return (after a few weeks and a few hundred dollars later) good-as-new. MuleSoft is the tractor guy. It's a Salesforce-owned integration and API platform that's excellent for working with

Gemini Spark updates: macOS launch, connected apps and more

The latest Gemini Spark updates brings Spark to the macOS app, connects with your favorite apps and tracks topics in real time.

Zapier vs. ChatGPT: When to use each (or both) [2026]

Comparing ChatGPT and Zapier might seem like comparing AI apples to automated oranges. But over the last couple of years, both platforms have picked up new agentic AI features, and now they share a lot of capabilities—and they combine into a delightful AI automation fruit juice. I've been using both tools every day for over three years, so I'm very keyed into the differences between the two, where each one shines, and how to run them together in ways that cut your token spend and make it safer f

The latest AI news we announced in June 2026

Here are Google’s latest AI updates from June 2026.

The Gemini app is bringing personalized image creation to more users.

Personal Intelligence makes the Gemini app feel tailored to you. With your permission, it pulls from Google tools like Gmail, Google Photos, YouTube and Search to provid…

Gemini can now take notes in Google Meet for Google AI Pro and Ultra subscribers.

Google Meet's "Take notes for me" feature is available to Google AI Pro and Ultra subscribers in select languages.

What is an AI agent?

When you think of AI agents, do you imagine a personal AI assistant like Tony Stark's Jarvis? Perhaps a calm-under-pressure TARS from Interstellar? Or, more on the scary spectrum, an amoral HAL 9000 straight out of 2001: A Space Odyssey? Current technology doesn't come close to that kind of science fiction (yet). But the field is evolving fast. What AI agents are capable of today looks nothing like it did even just a few months ago. Here's how AI agents actually work, what they can do right now

Take our I/O 2026 quiz, vibe coded in Google AI Studio.

We used Google AI Studio to vibe code a quiz about our top I/O 2026 announcements.

3 ways this coffee shop is growing with Gemini

Small businesses like coffee shops can use Gemini to save time on graphic design, email marketing and sales forecasting.

Pope Leo XIV Declares AI a Threat to Human Dignity and Workers’ Rights

Pope Leo XIV is taking a bold stance on artificial intelligence, calling it “a challenge to human dignity, justice and labour” in his first major address since being elected leader of the Catholic Church. The new pontiff is placing AI at the center of the Church’s moral agenda, warning that we’re entering a new industrial revolution with the same threats to workers and human rights seen over a century ago. “In our own day… developments in the field of artificial intelligence pose new challenges,” Leo said, addressing the College of Cardinals on Saturday in the New Synod Hall. He echoed The post Pope Leo XIV Declares AI a Threat to Human Dignity and Workers’ Rights appeared first on DailyAI.

Google faces another AI training lawsuit from major publishers

Hachette, Cengage, Elsevier, and other publishers allege that Google trained its AI on copyrighted works without the necessary permissions.

Celebrating 25 years of visual search innovation

Google Images is turning 25. Here’s a look back at some major milestones — and new ways to explore and create visual content.

Meta’s Adam Mosseri says AI token budgets could soon be capped per engineer

Instagram head Adam Mosseri believes companies will eventually need to manage AI token spending the same way they manage payroll or other operating expenses, predicting that engineers could soon face limits on how much they spend using AI tools.

Listen Labs raises $69M after viral billboard hiring stunt to scale AI customer interviews

Alfred Wahlforss was running out of options. His startup, Listen Labs, needed to hire over 100 engineers, but competing against Mark Zuckerberg's $100 million offers seemed impossible. So he spent $5,000 — a fifth of his marketing budget — on a billboard in San Francisco displaying what looked like gibberish: five strings of random numbers. The numbers were actually AI tokens. Decoded, they led to a coding challenge: build an algorithm to act as a digital bouncer at Berghain, the Berlin nightclub famous for rejecting nearly everyone at the door. Within days, thousands attempted the puzzle. 430 cracked it. Some got hired. The winner flew to Berlin, all expenses paid. That unconventional approach has now attracted $69 million in Series B funding, led by Ribbit Capital with participation from Evantic and existing investors Sequoia Capital, Conviction, and Pear VC. The round values Listen Labs at $500 million and brings its total capital to $100 million. In nine months since launch, the company has grown annualized revenue by 15x to eight figures and conducted over one million AI-powered interviews. "When you obsess over customers, everything else follows," Wahlforss said in an interview with VentureBeat. "Teams that use Listen bring the customer into every decision, from marketing to product, and when the customer is delighted, everyone is." Why traditional market research is broken, and what Listen Labs is building to fix it Listen's AI researcher finds participants, conducts in-depth interviews, and delivers actionable insights in hours, not weeks. The platform replaces the traditional choice between quantitative surveys — which provide statistical precision but miss nuance—and qualitative interviews, which deliver depth but cannot scale. Wahlforss explained the limitation of existing approaches: "Essentially surveys give you false precision because people end up answering the same question... You can't get the outliers. People are actually not honest on surveys." The alternative, one-on-one human interviews, "gives you a lot of depth. You can ask follow up questions. You can kind of double check if they actually know what they're talking about. And the problem is you can't scale that." The platform works in four steps: users create a study with AI assistance, Listen recruits participants from its global network of 30 million people, an AI moderator conducts in-depth interviews with follow-up questions, and results are packaged into executive-ready reports including key themes, highlight reels, and slide decks. What distinguishes Listen's approach is its use of open-ended video conversations rather than multiple-choice forms. "In a survey, you can kind of guess what you should answer, and you have four options," Wahlforss said. "Oh, they probably want me to buy high income. Let me click on that button versus an open ended response. It just generates much more honesty." The dirty secret of the $140 billion market research industry: rampant fraud Listen finds and qualifies the right participants in its global network of 30 million people. But building that panel required confronting what Wahlforss called "one of the most shocking things that we've learned when we entered this industry"—rampant fraud. "Essentially, there's a financial transaction involved, which means there will be bad players," he explained. "We actually had some of the largest companies, some of them have billions in revenue, send us people who claim to be kind of enterprise buyers to our platform and our system immediately detected, like, fraud, fraud, fraud, fraud, fraud." The company built what it calls a "quality guard" that cross-references LinkedIn profiles with video responses to verify identity, checks consistency across how participants answer questions, and flags suspicious patterns. The result, according to Wahlforss: "People talk three times more. They're much more honest when they talk about sensitive topics like politics and mental health." Emeritus, an online education company that uses Listen, reported that approximately 20% of survey responses previously fell into the fraudulent or low-quality category. With Listen, they reduced this to almost zero. "We did not have to replace any responses because of fraud or gibberish information," said Gabrielli Tiburi, Assistant Manager of Customer Insights at Emeritus. How Microsoft, Sweetgreen, and Chubbies are using AI interviews to build better products The speed advantage has proven central to Listen's pitch. Traditional customer research at Microsoft could take four to six weeks to generate insights. "By the time we get to them, either the decision has been made or we lose out on the opportunity to actually influence it," said Romani Patel, Senior Research Manager at Microsoft. With Listen, Microsoft can now get insights in days, and in many cases, within hours. The platform has already powered several high-profile initiatives. Microsoft used Listen Labs to collect global customer stories for its 50th anniversary celebration. "We wanted users to share how Copilot is empowering them to bring their best self forward," Patel said, "and we were able to collect those user video stories within a day." Traditionally, that kind of work would have taken six to eight weeks. Simple Modern, an Oklahoma-based drinkware company, used Listen to test a new product concept. The process took about an hour to write questions, an hour to launch the study, and 2.5 hours to receive feedback from 120 people across the country. "We went from 'Should we even have this product?' to 'How should we launch it?'" said Chris Hoyle, the company's Chief Marketing Officer. Chubbies, the shorts brand, achieved a 24x increase in youth research participation—growing from 5 to 120 participants — by using Listen to overcome the scheduling challenges of traditional focus groups with children. "There's school, sports, dinner, and homework," explained Lauren Neville, Director of Insights and Innovation. "I had to find a way to hear from them that fit into their schedules." The company also discovered product issues through AI interviews that might have gone undetected otherwise. Wahlforss described how the AI "through conversations, realized there were like issues with the the kids short line, and decided to, like, interview hundreds of kids. And I understand that there were issues in the liner of the shorts and that they were, like, scratchy, quote, unquote, according to the people interviewed." The redesigned product became "a blockbuster hit." The Jevons paradox explains why cheaper research creates more demand, not less Listen Labs is entering a massive but fragmented market. Wahlforss cited research from Andreessen Horowitz estimating the market research industry at roughly $140 billion annually, populated by legacy players — some with more than a billion dollars in revenue — that he believes are vulnerable to disruption. "There are very much existing budget lines that we are replacing," Wahlforss said. "Why we're replacing them is that one, they're super costly. Two, they're kind of stuck in this old paradigm of choosing between a survey or interview, and they also take months to work with." But the more intriguing dynamic may be that AI-powered research doesn't just replace existing spending — it creates new demand. Wahlforss invoked the Jevons paradox, an economic principle that occurs when technological advancements make a resource more efficient to use, but increased efficiency leads to increased overall consumption rather than decreased consumption. "What I've noticed is that as something gets cheaper, you don't need less of it. You want more of it," Wahlforss explained. "There's infinite demand for customer understanding. So the researchers on the team can do an order of magnitude more research, and also other people who weren't researchers before can now do that as part of their job." Inside the elite engineering team that built Listen Labs before they had a working toilet Listen Labs traces its origins to a consumer app that Wahlforss and his co-founder built after meeting at Harvard. "We built this consumer app that got 20,000 downloads in one day," Wahlforss recalled. "We had all these users, and we were thinking like, okay, what can we do to get to know them better? And we built this prototype of what Listen is today." The founding team brings an unusual pedigree. Wahlforss's co-founder "was the national champion in competitive programming in Germany, and he worked at Tesla Autopilot." The company claims that 30% of its engineering team are medalists from the International Olympiad in Informatics — the same competition that produced the founders of Cognition, the AI coding startup. The Berghain billboard stunt generated approximately 5 million views across social media, according to Wahlforss. It reflected the intensity of the talent war in the Bay Area. "We had to do these things because some of our, like early employees, joined the company before we had a working toilet," he said. "But now we fixed that situation." The company grew from 5 to 40 employees in 2024 and plans to reach 150 this year. It hires engineers for non-engineering roles across marketing, growth, and operations — a bet that in the AI era, technical fluency matters everywhere. Synthetic customers and automated decisions: what Listen Labs is building next Wahlforss outlined an ambitious product roadmap that pushes into more speculative territory. The company is building "the ability to simulate your customers, so you can take all of those interviews we've done, and then extrapolate based on that and create synthetic users or simulated user voices." Beyond simulation, Listen aims to enable automated action based on research findings. "Can you not just make recommendations, but also create spawn agents to either change things in code or some customer churns? Can you give them a discount and try to bring them back?" Wahlforss acknowledged the ethical implications. "Obviously, as you said, there's kind of ethical concerns there. Of like, automated decision making overall can be bad, but we will have considerable guardrails to make sure that the companies are always in the loop." The company already handles sensitive data with care. "We don't train on any of the data," Wahlforss said. "We will also scrub any sensitive PII automatically so the model can detect that. And there are times when, for example, you work with investors, where if you accidentally mention something that could be material, non public information, the AI can actually detect that and remove any information like that." How AI could reshape the future of product development Perhaps the most provocative implication of Listen's model is how it could reshape product development itself. Wahlforss described a customer — an Australian startup — that has adopted what amounts to a continuous feedback loop. "They're based in Australia, so they're coding during the day, and then in their night, they're releasing a Listen study with an American audience. Listen validates whatever they built during the day, and they get feedback on that. They can then plug that feedback directly into coding tools like Claude Code and iterate." The vision extends Y Combinator's famous dictum — "write code, talk to users" — into an automated cycle. "Write code is now getting automated. And I think like talk to users will be as well, and you'll have this kind of infinite loop where you can start to ship this truly amazing product, almost kind of autonomously." Whether that vision materializes depends on factors beyond Listen's control — the continued improvement of AI models, enterprise willingness to trust automated research, and whether speed truly correlates with better products. A 2024 MIT study found that 95% of AI pilots fail to move into production, a statistic Wahlforss cited as the reason he emphasizes quality over demos. "I'm constantly have to emphasize like, let's make sure the quality is there and the details are right," he said. But the company's growth suggests appetite for the experiment. Microsoft's Patel said Listen has "removed the drudgery of research and brought the fun and joy back into my work." Chubbies is now pushing its founder to give everyone in the company a login. Sling Money, a stablecoin payments startup, can create a survey in ten minutes and receive results the same day. "It's a total game changer," said Ali Romero, Sling Money's marketing manager. Wahlforss has a different phrase for what he's building. When asked about the tension between speed and rigor — the long-held belief that moving fast means cutting corners — he cited Nat Friedman, the former GitHub CEO and Listen investor, who keeps a list of one-liners on his website. One of them: "Slow is fake." It's an aggressive claim for an industry built on methodological caution. But Listen Labs is betting that in the AI era, the companies that listen fastest will be the ones that win. The only question is whether customers will talk back.

Here's how Gemini can help you avoid jetlag.

If you’ve got a faraway trip coming up, the Gemini app can help you avoid jetlag so you can make the most of your visit.Once you’ve given Gemini permission to access you…

The 6 best UiPath alternatives in 2026

My house was built in the '70s, but sometimes I swear that means the 1870s. Whenever I have a problem with the fixtures, I can't just hire a general electrician—I need someone who knows how to work around the archaic, nonsensical infrastructure that infects my home. Ideally, without ripping my entire house apart. My specialized electrician is basically what UiPath does for a company's tech stack. It's built to work around the ancient infrastructure—the mainframes, the Citrix environments, the d

Anthropic launches Cowork, a Claude Desktop agent that works in your files — no coding required

Anthropic released Cowork on Monday, a new AI agent capability that extends the power of its wildly successful Claude Code tool to non-technical users — and according to company insiders, the team built the entire feature in approximately a week and a half, largely using Claude Code itself. The launch marks a major inflection point in the race to deliver practical AI agents to mainstream users, positioning Anthropic to compete not just with OpenAI and Google in conversational AI, but with Microsoft's Copilot in the burgeoning market for AI-powered productivity tools. "Cowork lets you complete non-technical tasks much like how developers use Claude Code," the company announced via its official Claude account on X. The feature arrives as a research preview available exclusively to Claude Max subscribers — Anthropic's power-user tier priced between $100 and $200 per month — through the macOS desktop application. For the past year, the industry narrative has focused on large language models that can write poetry or debug code. With Cowork, Anthropic is betting that the real enterprise value lies in an AI that can open a folder, read a messy pile of receipts, and generate a structured expense report without human hand-holding. How developers using a coding tool for vacation research inspired Anthropic's latest product The genesis of Cowork lies in Anthropic's recent success with the developer community. In late 2024, the company released Claude Code, a terminal-based tool that allowed software engineers to automate rote programming tasks. The tool was a hit, but Anthropic noticed a peculiar trend: users were forcing the coding tool to perform non-coding labor. According to Boris Cherny, an engineer at Anthropic, the company observed users deploying the developer tool for an unexpectedly diverse array of tasks. "Since we launched Claude Code, we saw people using it for all sorts of non-coding work: doing vacation research, building slide decks, cleaning up your email, cancelling subscriptions, recovering wedding photos from a hard drive, monitoring plant growth, controlling your oven," Cherny wrote on X. "These use cases are diverse and surprising — the reason is that the underlying Claude Agent is the best agent, and Opus 4.5 is the best model." Recognizing this shadow usage, Anthropic effectively stripped the command-line complexity from their developer tool to create a consumer-friendly interface. In its blog post announcing the feature, Anthropic explained that developers "quickly began using it for almost everything else," which "prompted us to build Cowork: a simpler way for anyone — not just developers — to work with Claude in the very same way." Inside the folder-based architecture that lets Claude read, edit, and create files on your computer Unlike a standard chat interface where a user pastes text for analysis, Cowork requires a different level of trust and access. Users designate a specific folder on their local machine that Claude can access. Within that sandbox, the AI agent can read existing files, modify them, or create entirely new ones. Anthropic offers several illustrative examples: reorganizing a cluttered downloads folder by sorting and intelligently renaming each file, generating a spreadsheet of expenses from a collection of receipt screenshots, or drafting a report from scattered notes across multiple documents. "In Cowork, you give Claude access to a folder on your computer. Claude can then read, edit, or create files in that folder," the company explained on X. "Try it to create a spreadsheet from a pile of screenshots, or produce a first draft from scattered notes." The architecture relies on what is known as an "agentic loop." When a user assigns a task, the AI does not merely generate a text response. Instead, it formulates a plan, executes steps in parallel, checks its own work, and asks for clarification if it hits a roadblock. Users can queue multiple tasks and let Claude process them simultaneously — a workflow Anthropic describes as feeling "much less like a back-and-forth and much more like leaving messages for a coworker." The system is built on Anthropic's Claude Agent SDK, meaning it shares the same underlying architecture as Claude Code. Anthropic notes that Cowork "can take on many of the same tasks that Claude Code can handle, but in a more approachable form for non-coding tasks." The recursive loop where AI builds AI: Claude Code reportedly wrote much of Claude Cowork Perhaps the most remarkable detail surrounding Cowork's launch is the speed at which the tool was reportedly built — highlighting a recursive feedback loop where AI tools are being used to build better AI tools. During a livestream hosted by Dan Shipper, Felix Rieseberg, an Anthropic employee, confirmed that the team built Cowork in approximately a week and a half. Alex Volkov, who covers AI developments, expressed surprise at the timeline: "Holy shit Anthropic built 'Cowork' in the last... week and a half?!" This prompted immediate speculation about how much of Cowork was itself built by Claude Code. Simon Smith, EVP of Generative AI at Klick Health, put it bluntly on X: "Claude Code wrote all of Claude Cowork. Can we all agree that we're in at least somewhat of a recursive improvement loop here?" The implication is profound: Anthropic's AI coding agent may have substantially contributed to building its own non-technical sibling product. If true, this is one of the most visible examples yet of AI systems being used to accelerate their own development and expansion — a strategy that could widen the gap between AI labs that successfully deploy their own agents internally and those that do not. Connectors, browser automation, and skills extend Cowork's reach beyond the local file system Cowork doesn't operate in isolation. The feature integrates with Anthropic's existing ecosystem of connectors — tools that link Claude to external information sources and services such as Asana, Notion, PayPal, and other supported partners. Users who have configured these connections in the standard Claude interface can leverage them within Cowork sessions. Additionally, Cowork can pair with Claude in Chrome, Anthropic's browser extension, to execute tasks requiring web access. This combination allows the agent to navigate websites, click buttons, fill forms, and extract information from the internet — all while operating from the desktop application. "Cowork includes a number of novel UX and safety features that we think make the product really special," Cherny explained, highlighting "a built-in VM [virtual machine] for isolation, out of the box support for browser automation, support for all your claude.ai data connectors, asking you for clarification when it's unsure." Anthropic has also introduced an initial set of "skills" specifically designed for Cowork that enhance Claude's ability to create documents, presentations, and other files. These build on the Skills for Claude framework the company announced in October, which provides specialized instruction sets Claude can load for particular types of tasks. Why Anthropic is warning users that its own AI agent could delete their files The transition from a chatbot that suggests edits to an agent that makes edits introduces significant risk. An AI that can organize files can, theoretically, delete them. In a notable display of transparency, Anthropic devoted considerable space in its announcement to warning users about Cowork's potential dangers — an unusual approach for a product launch. The company explicitly acknowledges that Claude "can take potentially destructive actions (such as deleting local files) if it's instructed to." Because Claude might occasionally misinterpret instructions, Anthropic urges users to provide "very clear guidance" about sensitive operations. More concerning is the risk of prompt injection attacks — a technique where malicious actors embed hidden instructions in content Claude might encounter online, potentially causing the agent to bypass safeguards or take harmful actions. "We've built sophisticated defenses against prompt injections," Anthropic wrote, "but agent safety — that is, the task of securing Claude's real-world actions — is still an active area of development in the industry." The company characterized these risks as inherent to the current state of AI agent technology rather than unique to Cowork. "These risks aren't new with Cowork, but it might be the first time you're using a more advanced tool that moves beyond a simple conversation," the announcement notes. Anthropic's desktop agent strategy sets up a direct challenge to Microsoft Copilot The launch of Cowork places Anthropic in direct competition with Microsoft, which has spent years attempting to integrate its Copilot AI into the fabric of the Windows operating system with mixed adoption results. However, Anthropic's approach differs in its isolation. By confining the agent to specific folders and requiring explicit connectors, they are attempting to strike a balance between the utility of an OS-level agent and the security of a sandboxed application. What distinguishes Anthropic's approach is its bottom-up evolution. Rather than designing an AI assistant and retrofitting agent capabilities, Anthropic built a powerful coding agent first — Claude Code — and is now abstracting its capabilities for broader audiences. This technical lineage may give Cowork more robust agentic behavior from the start. Claude Code has generated significant enthusiasm among developers since its initial launch as a command-line tool in late 2024. The company expanded access with a web interface in October 2025, followed by a Slack integration in December. Cowork is the next logical step: bringing the same agentic architecture to users who may never touch a terminal. Who can access Cowork now, and what's coming next for Windows and other platforms For now, Cowork remains exclusive to Claude Max subscribers using the macOS desktop application. Users on other subscription tiers — Free, Pro, Team, or Enterprise — can join a waitlist for future access. Anthropic has signaled clear intentions to expand the feature's reach. The blog post explicitly mentions plans to add cross-device sync and bring Cowork to Windows as the company learns from the research preview. Cherny set expectations appropriately, describing the product as "early and raw, similar to what Claude Code felt like when it first launched." To access Cowork, Max subscribers can download or update the Claude macOS app and click on "Cowork" in the sidebar. The real question facing enterprise AI adoption For technical decision-makers, the implications of Cowork extend beyond any single product launch. The bottleneck for AI adoption is shifting — no longer is model intelligence the limiting factor, but rather workflow integration and user trust. Anthropic's goal, as the company puts it, is to make working with Claude feel less like operating a tool and more like delegating to a colleague. Whether mainstream users are ready to hand over folder access to an AI that might misinterpret their instructions remains an open question. But the speed of Cowork's development — a major feature built in ten days, possibly by the company's own AI — previews a future where the capabilities of these systems compound faster than organizations can evaluate them. The chatbot has learned to use a file manager. What it learns to use next is anyone's guess.

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Google just redesigned the search box for the first time in 25 years — here’s why it matters more than you think.

For a quarter century, the Google search box has been one of the most recognizable interfaces in computing: a thin white rectangle, a blinking cursor, a few typed words, and a list of blue links. On Tuesday, Google will formally retire that paradigm. At its annual I/O developer conference, Google announced a sweeping redesign of the search box itself — the literal text field where billions of queries begin every day — transforming it from a simple keyword input into a dynamic, AI-driven conversation starter that can accept text, images, PDFs, videos, and even open Chrome tabs as inputs. The company is also merging its AI Overviews and AI Mode features into a single, seamless search flow, eliminating the friction that previously forced users to choose between a traditional results page and an AI-forward experience. Liz Reid, Google's vice president and head of Search, called it "the biggest upgrade to our iconic search box since its debut over 25 years ago" during a press briefing on Monday. The announcement arrived alongside a blizzard of other news — new Gemini models, a personal AI agent called Spark, an intelligent shopping cart, a reimagined developer platform — but the search box redesign may prove to be the most consequential. It is the clearest signal yet that Google views the future of its flagship product not as a place where users type fragmented keywords, but as an interface where they hold open-ended, multimodal conversations with an AI system backed by the entire web. The new search box expands, accepts files, and coaches you on what to ask The changes show a fundamental shift in how Google expects people to interact with the product that generates the vast majority of Alphabet's revenue. The box itself now dynamically expands to accommodate longer, more conversational queries. Where the old interface subtly encouraged brevity — a narrow field suited to two- or three-word keyword strings — the new design invites users to fully articulate complex questions in granular detail. It also now supports multimodal inputs directly. Users can upload images, PDFs, files, and videos, or drag in content from Chrome tabs, right from the main search interface. Previously, some of these capabilities existed in AI Mode, but reaching them required extra steps. Now they sit at the primary entry point. Google is also deploying what it describes as an AI-powered query suggestion system that "goes beyond autocomplete." Rather than simply predicting the next word a user might type based on popular searches, the system helps users formulate complex, nuanced queries — essentially coaching them toward the kind of detailed questions that AI Mode handles best. The new search box is starting to roll out immediately in all countries and languages where AI Mode is available. Google is merging AI overviews and AI mode into one seamless experience Perhaps more significant than the box itself is the architectural change happening behind it. Google is unifying AI Overviews — the AI-generated summary panels that appear atop traditional search results — with AI Mode, the more immersive conversational search experience the company launched at I/O one year ago. Starting Tuesday, this merged experience will be live across mobile and desktop worldwide. A user can type a question, receive an AI Overview alongside traditional results, and then continue directly into a back-and-forth AI Mode conversation to ask follow-up questions — all without navigating to a separate interface. Reid explained the logic during the press briefing: the new AI search box is "an upgrade of our traditional search box, and so the results take you directly to main search rather than AI mode." She noted that while some power users actively sought out AI Mode, "for most users, they don't actually want to have to think about, do they want more of a traditional page or an AI-forward search experience." The goal, she said, was to ensure that "for most users, they don't have to think about where to go, they can just go to the search box they're familiar with, and it feels like they get the best experience afterwards." One billion users and doubling queries reveal how fast search behavior is shifting Google's decision to redesign the foundational interface of its most important product did not happen in a vacuum. The company shared a set of usage statistics during the briefing that reveal just how rapidly user behavior is already changing. AI Mode, which launched in the United States at I/O 2025, has surpassed one billion monthly users in its first year. AI Mode queries have been doubling every quarter since launch. AI Overviews, the lighter-weight AI summaries, now reach more than 2.5 billion monthly users. And overall search query volume hit an all-time high last quarter — a data point the company had previously disclosed on its earnings call. Sundar Pichai, Google's CEO, framed these figures as evidence that AI features are additive, not cannibalistic, to search usage. "When people use our AI-powered features in search, they use search more," he said. He added that he loves "how search has become less about individual queries and feels more like an ongoing conversation, giving users deeper insights and connecting you with the vastness of the web." Reid reinforced the point: "It's not just that people are searching more, it's that they're searching differently. They're fully expressing their questions in granular detail, asking those follow-up questions and searching across modalities." Gemini 3.5 Flash gives Google's AI search the speed it needs to work at scale Under the hood, the new search experience runs on Gemini 3.5 Flash, Google's newest AI model, which the company also introduced at I/O. Google upgraded AI Mode's underlying model to 3.5 Flash to deliver what Reid described as "an even more powerful AI search experience." Gemini 3.5 Flash is the workhorse of this year's announcements. Google claims it outperforms its previous frontier model, Gemini 3.1 Pro, on nearly all benchmarks while running four times faster in output tokens per second than comparable frontier models. Pichai described it as being "in a league of its own in the top right quadrant" of the Artificial Analysis index, which plots intelligence against speed — meaning it delivers near-frontier quality at dramatically lower latency. That speed matters enormously for search. A conversational AI search experience that feels sluggish would be dead on arrival for a product that serves billions of queries daily. By coupling the redesigned interface with a model optimized for both quality and throughput, Google is attempting to make AI-powered search feel as instantaneous as the old keyword experience — while being dramatically more capable. Search can now build interactive visuals and custom mini apps on the fly The redesigned search box is also the gateway to a set of new capabilities that push search far beyond text-based answers. Google announced what it calls "generative UI" — the ability for search to dynamically build custom widgets, interactive visualizations, and even mini applications in real time, tailored to a user's specific question. Reid offered a concrete example during the briefing: a user could ask "How do black holes affect space time?" and receive an interactive visual in an AI Overview that brings the concept to life. Follow-up questions would trigger the system to dynamically generate entirely new visuals in real time. This is possible, she explained, because of "a novel real-time code generation system we built in partnership with the Google DeepMind team" that runs on Gemini 3.5 Flash. Generative UI capabilities will roll out to everyone this summer, free of charge. But Google is going further still. For ongoing tasks — planning a wedding, organizing a move, tracking a fitness routine — users will be able to build what the company describes as customizable, stateful experiences within search, powered by its Antigravity development platform. These require no coding expertise. Users simply describe what they want in natural language, and search builds it. Those experiences will be available in coming months, starting with Google AI Pro and Ultra subscribers in the United States. AI agents that monitor the web around the clock are coming to search results The redesign also opens the door to what Google calls "information agents" — AI agents that users can configure directly within search to monitor the web 24/7 for specific conditions and deliver synthesized updates when those conditions are met. A user could, for example, set up an agent to track market movements in a particular sector with specific parameters. The agent would create a monitoring plan, tap into real-time finance data, and proactively notify the user when conditions are met — complete with links and context for further research. Other use cases include apartment hunting, tracking sneaker drops, or monitoring any topic a user cares about. Information agents will launch first for Google AI Pro and Ultra subscribers this summer. These agents sit within a much larger strategic pivot that Google articulated throughout the briefing: the company is going all-in on AI systems that don't just answer questions but proactively take actions on users' behalf. Beyond search, Google introduced Gemini Spark, a 24/7 personal AI agent that runs on dedicated virtual machines in Google Cloud. It unveiled the Universal Cart, an intelligent cross-merchant shopping cart. It announced the Agent Payments Protocol for agents to make secure purchases. And it expanded its Antigravity developer platform into a full ecosystem for building autonomous AI agents. Publishers, advertisers, and SEO professionals face a new reality The redesign raises profound questions for the sprawling ecosystem — publishers, advertisers, SEO professionals — that has been built around the old model of keyword search and blue links. If users increasingly express their needs as full, conversational sentences rather than fragmented keywords, the entire discipline of search engine optimization will need to evolve. Keyword-density strategies become less relevant when the AI is parsing natural language intent rather than matching strings. Content that answers deep, nuanced questions in authoritative ways becomes more valuable; content engineered to rank for two-word keyword fragments becomes less so. For publishers, the stakes are existential. AI Overviews already synthesize information from across the web and present it directly in search results, reducing the need for users to click through to source material. The new seamless AI Mode integration deepens that dynamic: users can now get an AI-generated answer and ask multiple follow-up questions without ever leaving the search page. Google has consistently maintained that its AI features drive more traffic to publishers, but the redesign puts that claim under renewed scrutiny as the search results page becomes more self-contained. For advertisers — who fund the vast majority of Google's revenue — the shift from keywords to conversations changes the calculus of ad targeting. Conversational queries contain richer intent signals, which could make ad targeting more precise and valuable. But they also create new ambiguities: when a user is in the middle of a multi-turn conversation with AI Mode, where does an ad naturally fit? Google did not detail changes to its advertising model during the briefing, but the structural shift in the interface will inevitably reshape how ads are surfaced and measured. The search box was always more than a product — it was a habit for billions of people There is a reason Google chose to redesign the search box rather than simply adding new features behind it. The search box is not just a product element at this point; it is a cultural artifact — one of the few pieces of digital infrastructure used by essentially the entire internet-connected world. Changing it sends an unmistakable message about where the company believes computing is headed. For 25 years, the search box trained billions of people to think in keywords — to compress their curiosity into the shortest possible string of words. The new box invites them to do the opposite: to think out loud, to upload what they're looking at, to ask follow-up questions, to let an AI system handle the compression. Pichai tied the company's broader ambitions to a striking statistic: Google's surfaces now process over 3.2 quadrillion tokens per month, up seven-fold from a year ago. The company expects capital expenditures of approximately $180 to $190 billion in 2026 — roughly six times the $31 billion it spent four years ago — largely to support the infrastructure required for this AI transformation. When asked about the future of traditional search, he was direct. "Search is the most used AI product in the world," he said. The blinking cursor in Google's search box still invites you to type. But after 25 years of teaching the world to speak in keywords, Google is now asking it to speak in sentences — and betting roughly $190 billion that it will.

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