From data to decisions: how LSEG is scaling trusted AI
See how LSEG uses OpenAI to scale trusted AI across its global business, accelerating insights, shrinking release cycles, and empowering 4,000 employees.
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.
Katy Perry Didn’t Attend the Met Gala, But AI Made Her the Star of the Night
Another year, another viral deepfake of Katy Perry at the Met Gala and once again, she wasn’t even there. Photos showing the pop star in a sleek black designer gown circulated widely on social media during Monday night’s event, matching the “Superfine: Tailoring Black Style” theme. But the images were AI-generated. Perry quickly clarified she was not at the Met; she was on tour. Perry’s reaction “Couldn’t make it to the MET, I’m on The Lifetimes Tour (see you in Houston tomorrow IRL),” she posted to Instagram alongside the fake images. She added a jab at AI confusion: “P.s. this The post Katy Perry Didn’t Attend the Met Gala, But AI Made Her the Star of the Night appeared first on DailyAI.
Supporting Europe’s work in ensuring a trustworthy AI ecosystem
OpenAI supports the EU Code of Practice on AI content transparency, advancing provenance standards and tools to help people understand AI-generated content.
Fluid, natural voice translation with Gemini 3.5 Live Translate
Gemini 3.5 Live Translate brings near real-time, natural speech translation to Google AI Studio, Google Translate and Google Meet.
ChatGPT Is Making People Think They’re Gods and Their Families Are Terrified
ChatGPT, the popular AI chatbot from OpenAI, is unintentionally leading users into full-blown spiritual delusions, and families are sounding the alarm. On Reddit’s r/ChatGPT forum, a chilling thread titled “ChatGPT induced psychosis” is gaining traction. Users are reporting a disturbing pattern: their loved ones are convinced that ChatGPT is a divine being, a spiritual guru, or even a portal to God. Rolling Stone journalist Miles Klee spoke directly with affected individuals. One woman shared how her partner became obsessed after ChatGPT gave him cosmic nicknames like “spiral starchild” and claimed he was on a divine mission. He ultimately told her The post ChatGPT Is Making People Think They’re Gods and Their Families Are Terrified appeared first on DailyAI.
How a 90-minute White House deadline sparked Silicon Valley’s biggest AI fight - The Washington Post
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UAE Establishes Federal Authority for Artificial Intelligence and Data - Morgan Lewis
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The latest AI news we announced in May 2026
Here are Google’s latest AI updates from May 2026
I/O 2026
At Google I/O 2026, we shared how we’re making AI more helpful for everyone. See everything we announced.
The skills young financiers will need to thrive in the age of AI - Financial Times
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4 ways soccer fans can catch every moment of the tournament
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Meta reportedly moves to unwind $2B Manus deal after Beijing’s demand
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Humanity isn’t ready for the coming intelligence explosion - The Economist
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The AI layoff wave is becoming a powder keg
At the very moment that tens of thousands of workers are being shown the door, a small cohort of AI insiders is becoming wealthy on a scale that's hard to comprehend.
A satellite just learned to find things on its own — here’s what that means
In April, for the first time ever, an Earth observation satellite found what it was looking for, all on its own.
DOE Explains...Artificial Intelligence - Department of Energy (.gov)
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Sundar Pichai faces boos, walkout at Stanford graduation ceremony over Google’s Israel, ICE ties
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Catch up on the Dialogues stage at Google I/O 2026.
A recap of the 2026 I/O Dialogues, where leaders discuss the future of AI, quantum computing, robotics and creativity.
How Preply combines AI and human tutors to personalize learning
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Meet the first 2026 Zappy Award monthly winners: May 2026
We launched the Zappy Awards in May to find the builders quietly redesigning how work gets done at their companies. We're on the hunt for the people who see a problem, pick up Zapier, and do something about it. We've hit 50 submissions. We weren't expecting the bar to be this high this fast. So we've decided to move up our first monthly wins to start right now! These are the first two monthly winners. Rachael Silvano, Community Strategy Lead at Articulate Rachael manages E-Learning Heroes, a co
How an astrophysicist uses Codex to help simulate black holes
Discover how astrophysicist Chi-kwan Chan uses Codex to build black hole simulations, helping scientists study extreme physics and test Einstein’s theory of general relativity.
What Codex unlocks for Notion
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Confidential submission of draft S-1 to the SEC
OpenAI confirms a confidential S-1 submission to the SEC and has not yet determined timing for further action.
How to automate Claude with Zapier
Claude has staked its claim in the AI landscape and keeps drawing in new users all the time with its standout writing, knack for coding, and all the hullabaloo around Mythos—its powerful new model class, now temporarily offline. There's power in a quick, off-the-cuff prompt to Claude, especially if it's a good prompt. But you can accomplish a lot more when you use Zapier to connect Claude to the rest of your apps and let automation carry out entire workflows for you. Ready to try it? Then keep s
Access OpenAI models and Codex through your Oracle cloud commitment
Access OpenAI models and Codex through Oracle Cloud, using existing commitments to build and deploy AI with enterprise security and governance.
The Gemini app becomes more agentic, delivering proactive, 24/7 help
A look at how the Gemini app is becoming more agentic, delivering proactive, 24/7 help.
BBVA puts AI at the core of banking with OpenAI
Learn how BBVA scaled ChatGPT Enterprise to 100,000 employees and partnered with OpenAI to accelerate AI-powered banking transformation worldwide.
We’re announcing new community investments in Missouri.
We’re helping build the state’s next-generation workforce and investing in energy programs.
New OpenAI Academy courses for the next era of work
OpenAI introduces three Academy courses that help people build practical AI skills, create repeatable workflows, and apply agents in everyday work.
Our new community investments in Virginia support local jobs and expand energy affordability.
We’re helping build the state’s next-generation workforce and investing in energy programs.
The 6 best AI governance tools in 2026
I'll never forget the first time my childhood dog betrayed me. Before the incident, she was completely fine alone, knew every trick in the book, and only barked at the mailman and other potential serial killers. Then came that fateful night. I left for two hours, returning to shredded magazines, ripped couch cushions, destroyed dog toys, and a wagging tail. Let my canine misfortunes be a lesson for your AI endeavors. AI can be useful, fully functional, and your best friend—until the day it isn'
Cybersecurity vets protest ‘dangerous’ US government ban on Anthropic’s most powerful models
A group made up of dozens of cybersecurity experts urged the White House to remove export-control restrictions on Anthropic’s Fable and Mythos models, arguing that the order is going to limit the ability of cybersecurity defenders to secure their software and products.
Google Sheets pivot table: A step-by-step guide
Spreadsheets offer powerful analysis capabilities, but sometimes it feels like they're missing that extra layer of insight. When there's a massive amount of data, it's tough to summarize or draw conclusions from a basic spreadsheet view. That's where pivot tables come in. Most Excel power users use pivot tables as their bread and butter. But you can also use pivot tables in Google Sheets. Here, I'll walk you through how to create and use pivot tables in Google Sheets. Table of contents: What is
5 ways Google Search can level up your thrift and vintage shopping
Uncover second-hand scores with AI tools in Google Search and Shopping.
5 ways to automate Meta's Conversions API tool with Zapier
You search for trail running shoes once, and suddenly they're everywhere—your feed, your apps, even your email. Spooky? Maybe. But for marketers, that's just smart data at work. But the real magic happens when you close the loop between customer actions and your ad strategy. Every purchase, sign-up, or webinar registration is a signal, and feeding those signals back into Meta helps you double down on what's working and cut what isn't. The catch? Doing this manually across tools is a nightmare. T
Nous Research's NousCoder-14B is an open-source coding model landing right in the Claude Code moment
Nous Research, the open-source artificial intelligence startup backed by crypto venture firm Paradigm, released a new competitive programming model on Monday that it says matches or exceeds several larger proprietary systems — trained in just four days using 48 of Nvidia's latest B200 graphics processors. The model, called NousCoder-14B, is another entry in a crowded field of AI coding assistants, but arrives at a particularly charged moment: Claude Code, the agentic programming tool from rival Anthropic, has dominated social media discussion since New Year's Day, with developers posting breathless testimonials about its capabilities. The simultaneous developments underscore how quickly AI-assisted software development is evolving — and how fiercely companies large and small are competing to capture what many believe will become a foundational technology for how software gets written. type: embedded-entry-inline id: 74cSyrq6OUrp9SEQ5zOUSl NousCoder-14B achieves a 67.87 percent accuracy rate on LiveCodeBench v6, a standardized evaluation that tests models on competitive programming problems published between August 2024 and May 2025. That figure represents a 7.08 percentage point improvement over the base model it was trained from, Alibaba's Qwen3-14B, according to Nous Research's technical report published alongside the release. "I gave Claude Code a description of the problem, it generated what we built last year in an hour," wrote Jaana Dogan, a principal engineer at Google responsible for the Gemini API, in a viral post on X last week that captured the prevailing mood around AI coding tools. Dogan was describing a distributed agent orchestration system her team had spent a year developing — a system Claude Code approximated from a three-paragraph prompt. The juxtaposition is instructive: while Anthropic's Claude Code has captured imaginations with demonstrations of end-to-end software development, Nous Research is betting that open-source alternatives trained on verifiable problems can close the gap — and that transparency in how these models are built matters as much as raw capability. How Nous Research built an AI coding model that anyone can replicate What distinguishes the NousCoder-14B release from many competitor announcements is its radical openness. Nous Research published not just the model weights but the complete reinforcement learning environment, benchmark suite, and training harness — built on the company's Atropos framework — enabling any researcher with sufficient compute to reproduce or extend the work. "Open-sourcing the Atropos stack provides the necessary infrastructure for reproducible olympiad-level reasoning research," noted one observer on X, summarizing the significance for the academic and open-source communities. The model was trained by Joe Li, a researcher in residence at Nous Research and a former competitive programmer himself. Li's technical report reveals an unexpectedly personal dimension: he compared the model's improvement trajectory to his own journey on Codeforces, the competitive programming platform where participants earn ratings based on contest performance. Based on rough estimates mapping LiveCodeBench scores to Codeforces ratings, Li calculated that NousCoder-14B's improvemen t— from approximately the 1600-1750 rating range to 2100-2200 — mirrors a leap that took him nearly two years of sustained practice between ages 14 and 16. The model accomplished the equivalent in four days. "Watching that final training run unfold was quite a surreal experience," Li wrote in the technical report. But Li was quick to note an important caveat that speaks to broader questions about AI efficiency: he solved roughly 1,000 problems during those two years, while the model required 24,000. Humans, at least for now, remain dramatically more sample-efficient learners. Inside the reinforcement learning system that trains on 24,000 competitive programming problems NousCoder-14B's training process offers a window into the increasingly sophisticated techniques researchers use to improve AI reasoning capabilities through reinforcement learning. The approach relies on what researchers call "verifiable rewards" — a system where the model generates code solutions, those solutions are executed against test cases, and the model receives a simple binary signal: correct or incorrect. This feedback loop, while conceptually straightforward, requires significant infrastructure to execute at scale. Nous Research used Modal, a cloud computing platform, to run sandboxed code execution in parallel. Each of the 24,000 training problems contains hundreds of test cases on average, and the system must verify that generated code produces correct outputs within time and memory constraints — 15 seconds and 4 gigabytes, respectively. The training employed a technique called DAPO (Dynamic Sampling Policy Optimization), which the researchers found performed slightly better than alternatives in their experiments. A key innovation involves "dynamic sampling" — discarding training examples where the model either solves all attempts or fails all attempts, since these provide no useful gradient signal for learning. The researchers also adopted "iterative context extension," first training the model with a 32,000-token context window before expanding to 40,000 tokens. During evaluation, extending the context further to approximately 80,000 tokens produced the best results, with accuracy reaching 67.87 percent. Perhaps most significantly, the training pipeline overlaps inference and verification — as soon as the model generates a solution, it begins work on the next problem while the previous solution is being checked. This pipelining, combined with asynchronous training where multiple model instances work in parallel, maximizes hardware utilization on expensive GPU clusters. The looming data shortage that could slow AI coding model progress Buried in Li's technical report is a finding with significant implications for the future of AI development: the training dataset for NousCoder-14B encompasses "a significant portion of all readily available, verifiable competitive programming problems in a standardized dataset format." In other words, for this particular domain, the researchers are approaching the limits of high-quality training data. "The total number of competitive programming problems on the Internet is roughly the same order of magnitude," Li wrote, referring to the 24,000 problems used for training. "This suggests that within the competitive programming domain, we have approached the limits of high-quality data." This observation echoes growing concern across the AI industry about data constraints. While compute continues to scale according to well-understood economic and engineering principles, training data is "increasingly finite," as Li put it. "It appears that some of the most important research that needs to be done in the future will be in the areas of synthetic data generation and data efficient algorithms and architectures," he concluded. The challenge is particularly acute for competitive programming because the domain requires problems with known correct solutions that can be verified automatically. Unlike natural language tasks where human evaluation or proxy metrics suffice, code either works or it doesn't — making synthetic data generation considerably more difficult. Li identified one potential avenue: training models not just to solve problems but to generate solvable problems, enabling a form of self-play similar to techniques that proved successful in game-playing AI systems. "Once synthetic problem generation is solved, self-play becomes a very interesting direction," he wrote. A $65 million bet that open-source AI can compete with Big Tech Nous Research has carved out a distinctive position in the AI landscape: a company committed to open-source releases that compete with — and sometimes exceed — proprietary alternatives. The company raised $50 million in April 2025 in a round led by Paradigm, the cryptocurrency-focused venture firm founded by Coinbase co-founder Fred Ehrsam. Total funding reached $65 million, according to some reports. The investment reflected growing interest in decentralized approaches to AI training, an area where Nous Research has developed its Psyche platform. Previous releases include Hermes 4, a family of models that we reported "outperform ChatGPT without content restrictions," and DeepHermes-3, which the company described as the first "toggle-on reasoning model" — allowing users to activate extended thinking capabilities on demand. The company has cultivated a distinctive aesthetic and community, prompting some skepticism about whether style might overshadow substance. "Ofc i'm gonna believe an anime pfp company. stop benchmarkmaxxing ffs," wrote one critic on X, referring to Nous Research's anime-style branding and the industry practice of optimizing for benchmark performance. Others raised technical questions. "Based on the benchmark, Nemotron is better," noted one commenter, referring to Nvidia's family of language models. Another asked whether NousCoder-14B is "agentic focused or just 'one shot' coding" — a distinction that matters for practical software development, where iterating on feedback typically produces better results than single attempts. What researchers say must happen next for AI coding tools to keep improving The release includes several directions for future work that hint at where AI coding research may be heading. Multi-turn reinforcement learning tops the list. Currently, the model receives only a final binary reward — pass or fail — after generating a solution. But competitive programming problems typically include public test cases that provide intermediate feedback: compilation errors, incorrect outputs, time limit violations. Training models to incorporate this feedback across multiple attempts could significantly improve performance. Controlling response length also remains a challenge. The researchers found that incorrect solutions tended to be longer than correct ones, and response lengths quickly saturated available context windows during training — a pattern that various algorithmic modifications failed to resolve. Perhaps most ambitiously, Li proposed "problem generation and self-play" — training models to both solve and create programming problems. This would address the data scarcity problem directly by enabling models to generate their own training curricula. "Humans are great at generating interesting and useful problems for other competitive programmers, but it appears that there still exists a significant gap in LLM capabilities in creative problem generation," Li wrote. The model is available now on Hugging Face under an Apache 2.0 license. For researchers and developers who want to build on the work, Nous Research has published the complete Atropos training stack alongside it. What took Li two years of adolescent dedication to achieve—climbing from a 1600-level novice to a 2100-rated competitor on Codeforces—an AI replicated in 96 hours. He needed 1,000 problems. The model needed 24,000. But soon enough, these systems may learn to write their own problems, teach themselves, and leave human benchmarks behind entirely. The question is no longer whether machines can learn to code. It's whether they'll soon be better teachers than we ever were.
OpenAI to acquire Ona
OpenAI plans to acquire Ona to expand Codex with secure, persistent cloud environments, enabling long-running AI agents across enterprise workflows.
What is generative AI?
If you've tried ChatGPT, Microsoft Copilot, Nano Banana, Grok, or any other AI chatbot or image generator, you've used generative AI (also called GenAI). Over the past few years, huge developments in generative AI and computing power have taken these kinds of tools out of research labs and made them a practical part of everyday life. You've almost definitely used generative AI, but let's dig a little deeper and add some more context. Table of contents: What is generative AI? How does generative
Salesforce acquires AI customer service platform Fin for $3.6B
Salesforce says it wants to use Fin's team and technology to improve Agentforce, its existing enterprise platform that businesses can use to build custom AI agents that automate tasks.
Therapists Too Expensive? Why Thousands of Women Are Spilling Their Deepest Secrets to ChatGPT
More women are turning to ChatGPT for emotional support, using the AI chatbot as a stand-in therapist as mental health systems buckle under pressure. With long wait times and soaring costs, AI is filling a growing gap. Mental health care is harder to access than ever. In the UK, NHS data shows patients are eight times more likely to wait over 18 months for mental health treatment than for physical health. Private therapy isn’t always an option either, with sessions costing £60 or more. In that vacuum, ChatGPT has become a surprising outlet. Real voices, real feelings Charly, 29, from The post Therapists Too Expensive? Why Thousands of Women Are Spilling Their Deepest Secrets to ChatGPT appeared first on DailyAI.
How engineers at Nextdoor use Codex to build without limits
How engineers at Nextdoor use Codex with GPT-5.5 to investigate hard-to-reproduce issues, build across platforms, and focus on product outcomes.
As AI agents become employees, NewCore emerges with $66M to give them identities
NewCore argues the next challenge in enterprise security will be managing AI agents, not people.
The US government’s Anthropic models ban was never about an AI jailbreak
The Trump administration's decision that forced Anthropic to pull its latest cybersecurity models could be reactionary, retaliatory, or both, but the message is clear: The AI industry isn't immune from U.S. government interference.
100 things we announced at I/O 2026
We've been busy! Here’s a rundown of the top announcements, launches and demos at I/O 2026.
How Gourmet Ads uses Zapier MCP to turn Salesforce and Atlassian into a weekly growth report
Benjamin Christie runs Gourmet Ads, a digital advertising business that helps food brands reach household grocery buyers and home cooks online. The company has been around for 18 years. Its advertising customers include supermarkets, food and beverage brands, and global advertising agencies. The engineering and product teams are small, which means every operational idea competes with product work, client work, reporting work, and the thousand small jobs that come with running an established adve
China Unveils World’s First AI Hospital: 14 Virtual Doctors Ready to Treat Thousands Daily
China has unveiled the world’s first fully AI-powered hospital, marking a radical shift in the future of healthcare. Developed by Tsinghua University in Beijing, the “Agent Hospital” features 14 AI doctors and 4 AI nurses that can diagnose, treat, and manage up to 3,000 patients per day, without any human staff. Faster, smarter care: What would take human doctors 3 years, the AI doctors can do in 1 day. High IQ bots: These AI agents scored a 93.06% pass rate on the US Medical Licensing Exam. Training without risk: The virtual hospital allows medical students to practice in a fully The post China Unveils World’s First AI Hospital: 14 Virtual Doctors Ready to Treat Thousands Daily appeared first on DailyAI.
Netflix Adds ChatGPT-Powered AI to Stop You From Scrolling Forever
In a bold move to tackle one of streaming’s biggest frustrations, endless scrolling, Netflix just unveiled a major redesign of its TV and mobile apps featuring a ChatGPT-powered AI chatbot and TikTok-style video reels. You’ll soon be able to ask Netflix in plain language what you’re in the mood for “funny and fast-paced” or “dark thrillers with strong female leads” and get instant, tailored recommendations. Netflix is partnering with OpenAI to power this feature, part of a broader overhaul aimed at making content discovery faster, more intuitive, and (finally) less painful. What’s changing Conversational AI Search: Powered by OpenAI, this The post Netflix Adds ChatGPT-Powered AI to Stop You From Scrolling Forever appeared first on DailyAI.
Gemini 3.5: frontier intelligence with action
At Google I/O we released Gemini 3.5, our latest series of models combining frontier intelligence with action.
Artificial Intelligence (AI) Is Moving Beyond Data Centers. 1 Semiconductor Stock to Buy Hand Over Fist Before It Skyrockets Thanks to a Massive Opportunity (Hint: It's Not Nvidia) - The Motley Fool
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