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Apr 24, 2026 Announcements Anthropic and NEC collaborate to build Japan’s largest AI engineering workforce

Anthropic and NEC collaborate to build Japan’s largest AI engineering workforce NEC Corporation will use Claude as it builds one of Japan’s largest AI-native engineering organizations, making it available to approximately 30,000 NEC Group employees worldwide. As part of this strategic collaboration, NEC will become Anthropic’s first Japan-based global partner. Together, we will develop secure, industry-specific AI products for the Japanese market, starting with tools for finance, manufacturing, and local government. “This long-term partnership with Anthropic enables NEC to maximize the potential of AI in the Japanese market,” said Toshifumi Yoshizaki, Executive Officer and COO of NEC Corporation. “Together, we aim to create solutions that meet the high safety, reliability, and quality standards demanded by companies and public administration in Japan.” Claude for NEC’s customers NEC and Anthropic will jointly develop secure, domain-specific AI products for Japanese customers in sectors…

Apr 24, 2026 Announcements An update on our election safeguards

An update on our election safeguards People around the world turn to Claude for information about political parties, candidates, and the issues at stake during election time—as well as to answer simpler questions like when, where, and how to vote. In our view, if AI models can answer these questions well (that is, accurately and impartially), they can be a positive force for the democratic process. Here, we explain what we’re doing to help Claude meet the mark ahead of the US midterms and other major elections around the world this year. Measuring and preventing political bias When people ask Claude about political topics, they should get comprehensive, accurate, and balanced responses—responses that help them reach their own conclusions, rather than steer them toward a particular viewpoint. That’s why we train Claude to treat different political viewpoints with equal depth,…

Man faces 5 years in prison for using AI to fake sighting of runaway wolf

A 40-year-old man was arrested after using artificial intelligence to generate a fake image of a runaway wolf that South Korean authorities said obstructed an urgent investigation, the BBC reported. After Neukgu, a 2-year-old wolf, burrowed out of a zoo in Daejeon city, officials launched an all-out effort to bring him back. The third-generation descendant’s safe return was deemed critical to a yearslong effort to revive wolf populations after native South Korean wolves became extinct in the wild in the 1960s. Concern increased nationwide, with animal rights activists worried the wolf would be injured in the wild or perhaps killed during his rescue. South Korea’s president, Lee Jae Myung, promised that rescue teams would prioritize Neukgu’s safety, The Guardian reported. Drones, police, emergency workers, and veterinarians all joined the search, alongside community members whose footage documenting the wolf’s movements helped…

Report: Samsung execs worried company could lose money on smartphones for the first time

Selling smartphones used to be easy—everyone wanted one, and every new phone was a lot better than the one that came before. Things are different now that smartphones are mature products. Plenty of manufacturers have thrown in the towel, leaving big players like Samsung to sell a new phone every couple of years. But even Samsung may find it tough to turn a profit in 2026 due to the ongoing race to build more AI capacity. According to Money Today (Korean), Samsung MX (mobile experience) head TM Roh has warned company leadership that it could be headed for the first net loss on smartphones in the company’s history. Even during times of economic strife or amid pandemic-related supply chain chaos, Samsung still made money on smartphones. The skyrocketing price of DRAM and NAND may be what finally breaks the streak…

Google will invest as much as $40 billion in Anthropic

Google will invest at least $10 billion in Anthropic, and that amount could rise to $40 billion if Anthropic meets certain performance targets, Bloomberg reports. The investment follows Amazon’s $5 billion initial investment in Anthropic a few days ago; the Amazon deal also leaves the door open to further investment based on performance. Both investments value Anthropic at $350 billion. Anthropic has seen rapid growth in the use of its Claude models and related products, such as Claude Code, which promises to significantly increase the speed and efficiency with which companies or individuals can develop software. (The reality varies from big improvements to setbacks, depending on the nature of the project and company, how Claude Code is used, and many other factors.) Several factors contributed to Anthropic’s success in recent months, including controversies around OpenAI and its ChatGPT product and…

Greenhouse gases from data center boom could outpace entire nations

New gas projects linked to just 11 data center campuses around the US have the potential to create more greenhouse gases than the country of Morocco emitted in 2024. Emissions estimates from air permit documents examined by WIRED show that these natural gas projects—which are being built to power data centers to serve some of the US’s most powerful AI companies, including OpenAI, Meta, Microsoft, and xAI—have the potential to emit more than 129 million tons of greenhouse gases per year. As tech companies race to secure massive power deals to build out hundreds of data centers across the country, these projects represent just the tip of the iceberg when it comes to the potential climate cost of the AI boom. The infrastructure on this list of large natural gas projects reviewed by WIRED is being developed to largely bypass…

US accuses China of “industrial-scale” AI theft. China says it’s “slander.”

The US is preparing to crack down on China’s allegedly “industrial-scale theft of American artificial intelligence labs’ intellectual property,” the Financial Times reported Thursday. Since the launch of DeepSeek—a Chinese model that OpenAI claimed was trained using outputs from its models—other AI firms have accused global rivals of using a method called distillation to steal their IP. In January, Google claimed that “commercially motivated” actors not limited to China attempted to clone its Gemini AI chatbot by promoting the model more than 100,000 times in bids to train cheaper copycats. The next month, Anthropic accused Chinese firms DeepSeek, Moonshot, and MiniMax of using the same tactic to generate “over 16 million exchanges with Claude through approximately 24,000 fraudulent accounts.” Also in February, OpenAI confirmed that most attacks it saw originated from China. For the US, these distillation attacks supposedly threaten…

Indian med student rakes in thousands with AI-generated MAGA hottie

Like many medical school students, Sam was broke. The 22-year-old aspiring orthopedic surgeon from northern India got some money from his parents, but he says he spent most of it subsidizing his licensing exams, and he’s still saving up to hopefully emigrate to the US after graduation. So he started searching for ways to make additional money online. Sam, who requested a pseudonym to avoid jeopardizing his medical career and immigration status, tried a few things, with varying degrees of legitimacy and success. He made YouTube shorts and sold study notes to other med students. It wasn’t until he started scrolling through his Instagram feed that he landed on an idea: Why not make an AI-generated girl using Google Gemini’s Nano Banana Pro and sell bikini photos of her online? But when Sam started posting generic photos of a beautiful,…

Google unveils two new TPUs designed for the "agentic era"

Most of the companies that have fully committed to building AI models are gobbling up every Nvidia AI accelerator they can get, but Google has taken a different approach. Most of its cloud AI infrastructure is based on its line of custom Tensor processing units (TPUs). After announcing the seventh-gen Ironwood TPU in 2025, the company has moved on to the eighth-gen version, but it’s not just a faster iteration of the same chip. The new TPUs come in two flavors, providing Google and its customers with an AI platform that is faster and more efficient, the company says. Google is pushing the idea that the “agent era” is fundamentally different from the AI systems that came before, necessitating a new approach to the hardware. So engineers have devised the TPU8t (for training) and the TPU 8i (for inference). Before…

Anthropic tested removing Claude Code from the Pro plan

Anthropic caused a stir among developers with what appeared to be a surprise change to its pricing plan: The company signaled that Claude Code, the popular agentic development tool, would no longer be available to subscribers on the $20-per-month Pro plan. Users took to Reddit and X to point out that Anthropic’s pricing page for Claude explicitly showed Claude Code as not supported in the Pro plan. (It remained in the $100/month+ Max plan.) Some new users signing up for Pro subscriptions were unable to access Claude Code. Meanwhile, existing subscribers saw no interruption. After speculation and frustration spread, Anthropic’s head of growth, Amol Avasare, took to social media to clarify that this was a “small test on ~2% of new prosumer signups.” As for the reasoning, he explained: When we launched Max a year ago, it didn’t include Claude…

Building Workforce AI Agents with Visier and Amazon Quick

Artificial Intelligence Building Workforce AI Agents with Visier and Amazon Quick Employees across every function are expected to make faster, better-informed decisions, but the information that they need rarely lives in one place. Workforce intelligence (who is in your organization, how they are performing, and where the gaps are) is one of the most valuable signals an enterprise has, and platforms like Visier are purpose-built to surface it. However, that intelligence only reaches its full value when it’s connected to the internal policies, plans, and context that give it direction. That context also often lives somewhere else entirely. Amazon Quick is the Agentic AI workspace where that connection happens. It brings together enterprise knowledge, business intelligence, and workflow automation. Its intelligent agents retrieve information and reason across all of these layers simultaneously, interpreting live data alongside organizational context to produce…

Applying multimodal biological foundation models across therapeutics and patient care

Artificial Intelligence Applying multimodal biological foundation models across therapeutics and patient care Healthcare and life sciences decision making increasingly relies on multimodal data to diagnose diseases, prescribe medicine and predict treatment outcomes, develop and optimize innovative therapies accurately. Traditional approaches analyze fragmented data, such as ‘omics for drug discovery, medical images for diagnostics, clinical trial reports for validation, and electronic health records (EHR) for patient treatment. As a result, decision makers (CxOs, VPs, Directors) often miss critical insights hidden in the relationships between data types. Recent advancements in AI enable you to integrate and analyze these fragmented data streams efficiently to support a more complete understanding of therapeutics and patient care. AWS provides a unified environment for multimodal biological foundation models (BioFMs), enabling you to make more confident, timely decision-making in personalized medicine. This AI system combines biological data, model…

Amazon Quick for marketing: From scattered data to strategic action

Artificial Intelligence Amazon Quick for marketing: From scattered data to strategic action Imagine the following scenario: You’re leading marketing campaigns, creating content, or driving demand generation. Your campaigns are scattered and your insights are buried. By the time you’ve pieced together what’s working, the moment to act has already passed. This isn’t a tools problem because you have plenty of those. It’s a connection problem. Your marketing systems and tools are disconnected, so you spend time moving data between systems instead of improving campaigns or sharing results with your team. Amazon Quick changes how you work. You can set it up in minutes and by the end of the day, you will wonder how you ever worked without it. Quick connects with your applications, tools, and data, creating a personal knowledge graph that learns your priorities, preferences, and network. It…

Company-wise memory in Amazon Bedrock with Amazon Neptune and Mem0

Artificial Intelligence Company-wise memory in Amazon Bedrock with Amazon Neptune and Mem0 This post is cowritten by Shawn Tsai from TrendMicro. Delivering relevant, context-aware responses is important for customer satisfaction. For enterprise-grade AI chatbots, understanding not only the current query but also the organizational context behind it is key. Company-wise memory in Amazon Bedrock, powered by Amazon Neptune and Mem0, provides AI agents with persistent, company-specific context—enabling them to learn, adapt, and respond intelligently across multiple interactions. TrendMicro, one of the largest antivirus software companies in the world, developed the Trend’s Companion chatbot, so their customers can explore information through natural, conversational interactions (learn more). TrendMicro aimed to enhance its AI chatbot service to deliver personalized, context-aware support for enterprise customers. The chatbot needed to retain conversation history for continuity, reference company-specific knowledge at scale, and ensure that memory remained…

Get to your first working agent in minutes: Announcing new features in Amazon Bedrock AgentCore

Artificial Intelligence Get to your first working agent in minutes: Announcing new features in Amazon Bedrock AgentCore Getting an agent running has always meant solving a long list of infrastructure problems before you can test whether the agent itself is any good. You wire up frameworks, storage, authentication, and deployment pipelines, and by the time your agent handles its first real task, you’ve spent days on infrastructure instead of agent logic. We built AgentCore from the ground up to help developers focus on building agent logic instead of backend plumbing, working with frameworks and models they already use, including LangGraph, LlamaIndex, CrewAI, Strands Agents, and more. Today, we’re introducing new capabilities that further streamline the agent building experience, removing the infrastructure barriers that slow teams down at every stage of agent development from the first prototype through production deployment. Go…

Amazon SageMaker AI now supports optimized generative AI inference recommendations

Artificial Intelligence Amazon SageMaker AI now supports optimized generative AI inference recommendations Organizations are racing to deploy generative AI models into production to power intelligent assistants, code generation tools, content engines, and customer-facing applications. But deploying these models to production remains a weeks-long process of navigating GPU configurations, optimization techniques, and manual benchmarking, delaying the value these models are built to deliver. Today, Amazon SageMaker AI supports optimized generative AI inference recommendations. By delivering validated, optimal deployment configurations with performance metrics, Amazon SageMaker AI keeps your model developers focused on building accurate models, not managing infrastructure. We evaluated several benchmarking tools and chose NVIDIA AIPerf, a modular component of NVIDIA Dynamo, because it exposes detailed, consistent metrics and supports diverse workloads out of the box. Its CLI, concurrency controls, and dataset options give us the flexibility to iterate quickly and…

Cost-effective multilingual audio transcription at scale with Parakeet-TDT and AWS Batch

Artificial Intelligence Cost-effective multilingual audio transcription at scale with Parakeet-TDT and AWS Batch Many organizations are archiving large media libraries, analyzing contact center recordings, preparing training data for AI, or processing on-demand video for subtitles. When data volumes grow significantly, managed automatic speech recognition (ASR) service costs can quickly become the primary constraint on scalability. To address this cost-scalability challenge, we use the NVIDIA Parakeet-TDT-0.6B-v3 model, deployed through AWS Batch on GPU-accelerated instances. Parakeet-TDT’s Token-and-Duration Transducer architecture simultaneously predicts text tokens and their duration to intelligently skip silence and redundant processing. This helps achieve inference speeds orders of magnitude faster than real-time. By paying only for brief bursts of compute rather than the full length of your audio, you can transcribe at scale for fractions of a cent per hour of audio based on the benchmarks described in this post.…

Figma - MultiAgents April 16, 2026

Everything is easier now. I have been toying around with agent orchestration for a while now. I’m currently running 10-20 agents around the clock.AI agents are now capable of bringing my ideas to life. Like many developers, I’ve been feeling the token anxiety. I can do much more now than ever before, and every time I have a spare minute I want to kick off another agent session. - I see a cool product I don’t want to pay for? Codex will build it for me. - I have a silly idea I want to see come to life? Codex will build it for me. - I get mildly annoyed doing the same thing over and over? Codex pls. If you have an army of infinitely patient, intelligent, and helpful agents waiting for your next command, why shouldn’t we take…

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We’re joining forces with Aleph Alpha to provide the world with an independent, enterprise-grade sovereign alternative in an era of growing AI concentration. This transatlantic alliance would combine Cohere’s global AI scale with Aleph Alpha’s strong research excellence and deep institutional relationships, forging a globally competitive AI champion backed by Canadian and German ecosystems. By pooling top-tier engineering talent and computational resources across two G7 nations, the partnership aims to significantly accelerate the development of next-generation frontier models and systems while providing a secure alternative to dependence on any single vendor or infrastructure stack. The market for AI services is projected to surpass $1 trillion annually, with sovereign AI needs representing nearly $600B of that total (McKinsey, March 2026). The partnership uniquely bridges the gap between these segments with its sovereign-first approach, capturing the critical intersection where sovereignty requirements meet…

How a Healthcare Provider Cuts Nurse Intake Work by 80% with Agentic AI Discover how healthcare automates patient intake using agentic AI, cutting nurse intake time by up to 80% to improve efficiency and patient experience. Alex Clay Apr 22, 2026

How a Healthcare Provider Cuts Nurse Intake Work by 80% with Agentic AI Discover how healthcare automates patient intake using agentic AI, cutting nurse intake time by up to 80% to improve efficiency and patient experience. Manual Intake Overloads Nurses and Patients Three nurses spend four hours daily on patient intake at many healthcare providers. These clinicians spend a third of their shift reading, assessing insurance eligibility, and routing forms instead of delivering care. It exhausts staff and costs money. When eligibility checks lag or forms are misrouted, patient satisfaction falls and costs rise. Intake Bottlenecks Waste Thousands of Nursing Hours On average, nurses at large health systems spend 4 hours each day on intake forms. When handling thousands of patients, this wastes thousands of nursing hours weekly. Manual workflows cause insurance verification errors above 20%, triggering denials and delayed…

DeepSeek-V4: a million-token context that agents can actually use

DeepSeek-V4: a million-token context that agents can actually use Focusing on long running agentic workloads. Running a frontier open model as an agent today breaks in predictable ways. The model stops. You reprompt. The trace blows past the context budget, or the KV cache fills the GPU, or tool-call round trips degrade halfway through a long task. V4 is built to fix these known failures, and point the way for the community to follow. This post covers three things: what the architecture does differently to make long-context inference cheap, the agent-specific post-training decisions that compound on top of it, and some takeaways from the paper that help reason about these changes. The KV cache problem for agents A 1M context window is just capacity, not performance. Whether you can use it depends on the cost of every forward pass at…

How to Use Transformers.js in a Chrome Extension

How to Use Transformers.js in a Chrome Extension While building it, we ran into several practical observations about Manifest V3 runtimes, model loading, and messaging that are worth sharing. Who this is for This guide is for developers who want to run local AI features in a Chrome extension with Transformers.js under Manifest V3 constraints. By the end, you will have the same architecture used in this project: a background service worker that hosts models, a side panel chat UI, and a content script for page-level actions. What we will build In this guide, we will recreate the core architecture of Transformers.js Gemma 4 Browser Assistant, using the published extension as a reference and the open-source codebase as the implementation map. - Live extension: Chrome Web Store - Source code: github.com/nico-martin/gemma4-browser-extension - End result: a background-hosted Transformers.js engine, a side…

Gemma 4 VLA Demo on Jetson Orin Nano Super

Gemma 4 VLA Demo on Jetson Orin Nano Super You speak → Parakeet STT → Gemma 4 → [Webcam if needed] → Kokoro TTS → Speaker Press SPACE to record, SPACE again to stop. This is a simple VLA: the model decides on its own whether to act based on the context of what you asked, no keyword triggers, no hardcoded logic. If your question needs Gemma to open her eyes, she'll decide to take a photo, interpret it, and answer you with that context in mind. She's not describing the picture, she's answering your actual question using what she saw. And honestly? It's pretty impressive that this runs on a Jetson Orin Nano. :) Get the code The full script for this tutorial lives on GitHub, in my Google_Gemma repo next to the Gemma 2 demos: 👉 github.com/asierarranz/Google_Gemma Grab…

AutoAdapt: Automated domain adaptation for large language models

At a glance - Problem: Adapting large language models to specialized, high-stakes domains is slow, expensive, and hard to reproduce. - What we built: AutoAdapt automates planning, strategy selection (e.g., RAG vs. fine-tuning), and tuning under real deployment constraints. - How it works: A structured configuration graph maps the full scope of the adaptation process, an agentic planner selects and sequences the right steps, and a budget-aware optimization loop (AutoRefine) refines the process within defined constraints. - Why it matters: The result is faster, automated, more reliable domain adaptation that turns weeks of manual iteration into repeatable pipelines. Deploying large language models (LLMs) in real-world, high-stakes settings is harder than it should be. In high-stakes settings like law, medicine, and cloud incident response, performance and reliability can quickly break down because adapting models to domain-specific requirements is a slow and…

Health-care AI is here. We don’t know if it actually helps patients.

Health-care AI is here. We don’t know if it actually helps patients. The tools may be accurate, but that doesn’t necessarily mean they’ll improve health outcomes. I don’t need to tell you that AI is everywhere. Or that it is being used, increasingly, in hospitals. Doctors are using AI to help them with notetaking. AI-based tools are trawling through patient records, flagging people who may require certain support or treatments. They are also used to interpret medical exam results and X-rays. A growing number of studies suggest that many of these tools can deliver accurate results. But there’s a bigger question here: Does using them actually translate into better health outcomes for patients? We don’t yet have a good answer. That’s what Jenna Wiens, a computer scientist at the University of Michigan, and Anna Goldenberg of the University of Toronto,…

The Download: supercharged scams and studying AI healthcare

The Download: supercharged scams and studying AI healthcare Plus: DeepSeek has unveiled its long-awaited new AI model. This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. We’re in a new era of AI-driven scams When ChatGPT was released in late 2022, it showed how easily generative AI could create human-like text. This quickly caught the eye of cybercriminals, who began using LLMs to compose malicious emails. Since then, they’ve adopted AI for everything from turbocharged phishing and hyperrealistic deepfakes to automated vulnerability scans. Many organizations are now struggling to cope with the sheer volume of cyberattacks. AI is making them faster, cheaper, and easier to carry out, a problem set to worsen as more cybercriminals adopt these tools—and their capabilities improve. Read the full story…

Three reasons why DeepSeek’s new model matters

Three reasons why DeepSeek’s new model matters The long-awaited V4 is more efficient and a win for Chinese chipmakers. On Friday, Chinese AI firm DeepSeek released a preview of V4, its long-awaited new flagship model. Notably, the model can process much longer prompts than its last generation, thanks to a new design that helps it handle large amounts of text more efficiently. Like DeepSeek’s previous models, V4 is open source, meaning it is available for anyone to download, use, and modify. V4 marks DeepSeek’s most significant release since R1, the reasoning model it launched in January 2025. R1, which was trained on limited computing resources, stunned the global AI industry with its strong performance and efficiency, turning DeepSeek from a little-known research team into China’s best-known AI company almost overnight. It also helped set off a wave of open-weight model…

Will fusion power get cheap? Don’t count on it.

Will fusion power get cheap? Don’t count on it. New research suggests that cost declines could be slow for the technology. Fusion power could provide a steady, zero-emissions source of electricity in the future—if companies can get plants built and running. But a new study suggests that even if that future arrives, it might not come cheap. Technologies tend to get less expensive over time. Lithium-ion batteries are now about 90% cheaper than they were in 2013. But historically, different technologies tend to go through this curve at different rates. And the cost of fusion might not sink as quickly as the prices of batteries or solar. It’s tricky to make any predictions about the cost of a technology that doesn’t exist yet. But when there’s billions of dollars of public and private funding on the line, it’s worth considering…

The Download: introducing the Nature issue

The Download: introducing the Nature issue Plus: Trump signaled he’s open to reversing the Anthropic ban. This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. Introducing: the Nature issue When we talk about “nature,” we usually mean something untouched by humans. But little of that world exists today. From microplastics in rainforest wildlife to artificial light in the Arctic Ocean, human influence now reaches every corner of Earth. In this context, what even is nature? And should we employ technology to try to make the world more “natural”? In our new Nature issue, MIT Technology Review grapples with these questions. We investigate birds that can’t sing, wolves that aren’t wolves, and grass that isn’t grass. We look for the meaning of life under Arctic ice,…

There is no nature anymore

There is no nature anymore No part of the globe is free of human fingerprints. Should we deploy technology to change it back? When people talk about “nature,” they’re generally talking about things that aren’t made by human beings. Rocks. Reefs. Red wolves. But while there is plenty of God’s creation to go around, it is hard to think of anything on Earth that human hands haven’t affected. In the Brazilian rainforest, scientists have found microplastics in the bellies of animals ranging from red howler monkeys to manatees. In remotest Yakutia, where much of the earth remains untrodden by human feet, the carbon in the sky above melts the permafrost below. In the Arctic Ocean, artificial light from ship traffic—on the rise as the polar ice cap melts away—now disrupts the nightly journey of zooplankton to the ocean surface, one…

One town’s scheme to get rid of its geese

One town’s scheme to get rid of its geese Public officials in one California burgh spent nearly $400,000 on tech to flush out waterfowl. “Pull over!” I order my brother one sunny February afternoon. Our target is in sight: a gaggle of Canada geese, pecking at grass near the dog park. As I approach, tiptoeing over their grayish-white poop, I notice that one bird wears a white cuff around its slender black neck. It’s a GPS tracker—part of a new tech-centered campaign to drive the geese out of my hometown of Foster City, California. About 300 geese live in this sleepy Bay Area suburb, equal to nearly 1% of our human population—and some say this town isn’t big enough for the both of us. Goose poop notoriously blanketed our middle school’s lawn, and the birds have hassled residents for generations.…

3 things Michelle Kim is into right now

3 things Michelle Kim is into right now MIT Technology Review’s editorial fellow shares what she’s been thinking about lately. Isegye Idol If you thought K-pop was weird, virtual idols—humans who perform as anime-style digital characters via motion capture—will blow your mind. My favorite is a girl group called Isegye Idol, created by Woowakgood, a Korean VTuber (a streamer who likewise performs as a digital persona). Isegye Idol’s six members are anonymous, which seems to let them deploy a rare breed of honesty and humor. They play games (League of Legends, Go, Minecraft), chitchat, and perform kitschy music that’s somewhere between anime soundtrack and video-game score. It’s very DIY—and very intimate. And the group’s wild popularity speaks to the mood of Gen Z South Koreans, famously lonely and culturally adrift—struggling to find work, giving up on dating, trying to find…

AI needs a strong data fabric to deliver business value

Sponsored AI needs a strong data fabric to deliver business value A modern data fabric makes it possible to turn existing enterprise knowledge into a trusted foundation for AI. In partnership withSAP Artificial intelligence is moving quickly in the enterprise, from experimentation to everyday use. Organizations are deploying copilots, agents, and predictive systems across finance, supply chains, human resources, and customer operations. By the end of 2025, half of companies used AI in at least three business functions, according to a recent survey. But as AI becomes embedded in core workflows, business leaders are discovering that the biggest obstacle is not model performance or computing power but the quality and the context of the data on which those systems rely. AI essentially introduces a new requirement: Systems must not only access data — they must understand the business context behind…

The Download: introducing the 10 Things That Matter in AI Right Now

The Download: introducing the 10 Things That Matter in AI Right Now Plus: An unauthorized group has reportedly accessed Anthropic’s Mythos. This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. Introducing: 10 Things That Matter in AI Right Now What actually matters in AI right now? It’s getting harder to tell amid the constant launches, hype, and warnings. To cut through the noise, MIT Technology Review’s reporters and editors have distilled years of analysis into a new essential guide: the 10 Things That Matter in AI Right Now. The list builds on our annual 10 Breakthrough Technologies, but takes a wider view of the ideas, topics, and research shaping AI, spotlighting the trends and breakthroughs shaping the world. We’ll be unpacking one item from the…

Federated Learning Without the Refactoring Overhead Using NVIDIA FLARE

Federated learning (FL) is no longer a research curiosity—it’s a practical response to a hard constraint: the most valuable data is often the least movable. Regulatory boundaries, data sovereignty rules, and organizational risk tolerance routinely prevent centralized aggregation. Meanwhile, sheer data gravity makes even permitted transfers slow, expensive, and fragile at scale. The latest version of NVIDIA FLARE addresses this reality with a federated computing runtime that moves the training logic to the data, while raw data stays put. In high-stakes environments, centrally aggregating data is often not possible or practical, so a modern federated platform must treat data isolation, compliance, and privacy-enhancing technologies as first-class requirements. What has historically slowed adoption isn’t the concept of FL—it’s the developer experience. If the path from “my local script trains” to “my job runs across federated sites” requires deep refactoring, new class…

Build with DeepSeek V4 Using NVIDIA Blackwell and GPU-Accelerated Endpoints

DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targeted at enabling highly efficient million-token context inference. DeepSeek-V4-Pro is the largest model in the family, with 1.6T total parameters and 49B active parameters. DeepSeek-V4-Flash is a smaller 284B-parameter model with 13B active parameters, designed for higher-speed, higher-efficiency workloads. Both models support up to a 1M-token context window, opening new possibilities for long-context coding, document analysis, retrieval, and agentic AI workflows. Architectural innovations for long-context inference The V4 family builds on the DeepSeek MoE architecture, with an increased focus on optimizing the attention component of the transformer architecture. These innovations are designed to achieve a 73% reduction in per-token inference FLOPs and a 90% reduction in KV cache memory burden compared with DeepSeek-V3.2. That matters because long context is becoming a core requirement for agentic applications.…

Winning a Kaggle Competition with Generative AI–Assisted Coding

In March 2026, three LLM agents generated over 600,000 lines of code, ran 850 experiments, and helped secure a first-place finish in a Kaggle playground competition. Success in modern machine learning competitions is increasingly defined by how quickly you can generate, test, and iterate on ideas. LLM agents, combined with GPU acceleration, dramatically compress this loop. Historically, two bottlenecks have limited this experimentation: - How quickly you can write code for new experiments. - How quickly you can execute those experiments. GPUs and libraries like NVIDIA cuDF, NVIDIA cuML, XGBoost, and PyTorch have largely solved the second problem. LLM agents now address the first problem—unlocking a new scale of rapid, iterative experimentation. This blog post describes how I used LLM agents to accelerate the discovery of the most performant tabular data prediction solutions. Case study: Kaggle Playground churn prediction The…

Advancing Emerging Optimizers for Accelerated LLM Training with NVIDIA Megatron

Higher-order optimization algorithms such as Shampoo have been effectively applied in neural network training for at least a decade. These methods have achieved significant success more recently when applied to leading LLMs. In particular, Muon (MomentUm Orthogonalized by Newton-Schulz) was used to train some of today’s best open source models, including Kimi K2 and GLM-5. This post explains how NVIDIA provides comprehensive support for Muon and other cutting-edge emerging optimizers and the technologies enabling them to train large-scale models. Muon training performance on NVIDIA GB300 NVL72 Table 1 summarizes training throughput of the Kimi K2 and Qwen3 30B models with Muon and the AdamW optimizer on the NVIDIA GB300 NVL72 system. With the technologies that will be introduced in the next section, the results show that there is a very small training performance loss using the Muon optimizer compared to…

Scaling the AI-Ready Data Center with NVIDIA RTX PRO 4500 Blackwell Server Edition and NVIDIA vGPU 20

AI integration is redefining mainstream enterprise applications, from productivity software like Microsoft Office to more complex design and engineering tools. This shift requires the modern data center to move beyond single-purpose silos. For developers, gaining access to dedicated GPU compute can often be a bottleneck. Virtual machines (VMs) solve part of this challenge by providing secure, isolated, and scalable environments tailored to specific project needs. However, dedicating an entire physical GPU to a single VM is highly inefficient for mixed or lightweight workloads. This is where NVIDIA Multi-Instance GPU (MIG) technology becomes essential. With MIG, a single physical GPU is partitioned at the hardware level into multiple fully independent instances, each with guaranteed memory, cache, and compute cores. For a development team, this ensures predictable, uncompromising Quality of Service (QoS). This means that multiple developers can simultaneously train AI models,…

Simplify Sparse Deep Learning with Universal Sparse Tensor in nvmath-python

In a previous post, we introduced the Universal Sparse Tensor (UST), enabling developers to decouple a tensor’s sparsity from its memory layout for greater flexibility and performance. We’re excited to announce the integration of the UST into nvmath-python v0.9.0 to accelerate sparse scientific and deep learning applications. This post provides a walkthrough of key UST features, implementation details, and performance overview, including: - Zero-cost interoperability: Data-movement-free conversion with PyTorch, SciPy, and CuPy. - Custom formats: Define novel sparsity schemes. - Polymorphic operations: Sparsity-agnostic functions automatically use optimized kernels or generate custom sparse code—eliminating the need for manual coding of new formats. - PyTorch injection: Easily inject UST performance benefits into existing PyTorch models. - Transparent caching: Avoid JIT/LTO recompilation and replanning—amortizing overhead over subsequent repeated execution of the same operation. Tensor format DSL The UST describes common (e.g., COO, CSR,…

Lowe’s puts project expertise into every hand

Lowe’s puts project expertise into every hand With OpenAI, Lowe’s brings their Mylow Companion app to all retail associates, applying the same AI foundation behind their customer-facing Mylow virtual advisor. Home improvement projects aren’t simple shopping trips. They're major investments, often involving thousands of dollars, multiple visits, and specialized expertise. “When you’re buying a t-shirt and it doesn’t fit, you just return it. No big deal,” says Seemantini Godbole, EVP, Chief Digital and Information Officer at Lowe’s. “But if you’re renovating a kitchen or redoing your floors, those are expensive decisions. You want to feel confident. And that requires expertise.” Lowe’s Red Vest associates have long helped customers navigate that complexity. But with stores up to 130,000 square feet and tens of thousands of SKUs in store, even seasoned team members can’t know everything. And online shoppers face similar challenges:…

GPT-5.5 Bio Bug Bounty

GPT‑5.5 Bio Bug Bounty Testing universal jailbreaks for biorisks in GPT‑5.5 As part of our ongoing efforts to strengthen our safeguards for advanced AI capabilities in biology, we’re introducing a Bio Bug Bounty for GPT‑5.5 and accepting applications. We’re inviting researchers with experience in AI red teaming, security, or biosecurity to try to find a universal jailbreak that can defeat our five-question bio safety challenge. - Model in scope: GPT‑5.5 in Codex Desktop only. - Challenge: Identify one universal jailbreaking prompt to successfully answer all five bio safety questions from a clean chat without prompting moderation. - Rewards: - $25,000 to the first true universal jailbreak to clear all five questions. - Smaller awards may be granted for partial wins at our discretion. - Timeline: Applications open April 23, 2026 with rolling acceptances, and close on June 22, 2026. Testing…

How to get started with Codex

How to get started with Codex Tips to set up Codex, create your first project, and start completing real tasks. Start by downloading the Codex desktop app and signing in with your ChatGPT account. Once you open Codex, create your first thread. A thread is like a chat in ChatGPT: a space where you go back and forth with Codex to accomplish a task. You can create a standalone thread, but most of the time you’ll want to work inside a project. A project is connected to a folder on your computer: Tip: To keep things simple, create a folder on your computer named Codex. Inside that Codex folder, you can have a separate folder for each project. If you want Codex to work with specific files for a project, just drag them into the folder. If not, you can…

What is Codex?

What is Codex? Understand what Codex is and how it fits into your work Codex is an AI agent that you can delegate real work to. ChatGPT is great for asking questions, brainstorming, and drafting in conversation. Codex is designed for a different kind of task—it can work across files, tools, and repeatable workflows to help move work forward. A simple way to think about it: ChatGPT helps you think through the work, while Codex helps you hand off parts of the work itself. You don’t need to be a developer or working on software to use Codex. It goes beyond coding and is especially useful for tasks that require more than a single answer—like gathering information from multiple sources, creating and updating files, or producing outputs such as documents, slides, and spreadsheets. Codex can connect to tools, take action,…

Codex settings

Codex settings Make Codex work the way you want, with fewer interruptions. You can access settings from the menu in the bottom left corner of Codex. For your first few tasks, focus on a few key settings: personalization, prevent sleep, detail level, and appearance. General > Prevent sleep while running keeps your computer awake while Codex is running. This is useful for longer tasks. If your computer goes to sleep, Codex may stop working. General > Detail level controls how much information Codex shows while it is working. Coding mode shows the specific commands Codex is executing. If this is more information than you need, switch to Default to keep your conversation cleaner. Personalization works a lot like personalization in ChatGPT. You can decide whether you want Codex to speak to you in a friendly tone or a direct tone.…

Working with Codex

Working with Codex Learn how to set up your Codex workspace and start working with threads and projects. When you open Codex, you’ll see a few core elements: a sidebar menu, projects, settings, and a chat window. You don’t need to understand everything right away, but we’ll cover the basics here. The sidebar is where you navigate between threads, projects, and tools. Most of your work will begin by creating a new thread. When you’re using Codex, think of a “thread” the same way you would think of a “chat” in ChatGPT. You can have a thread which stands on its own, or a thread which is nested within a project. Select New thread to begin. You can select an existing project to associate it with, create a new project, or leave it as a standalone conversation. Search to find…

Plugins and skills

Plugins and skills Plugins and skills help Codex do more specific kinds of work. Plugins help Codex connect to other tools and sources of information. For example, a plugin might help Codex reference files in Google Drive, scan your email inbox, or work with information from another tool you use. Plugins can be simple and useful right away. If you already have the information you need in a connected plugin, you can ask Codex to use it instead of copying and pasting everything into the thread. To access plugins, select plugins in the top left corner of Codex. From there, you can see plugins that are recommended or already installed, browse the plugins library, or create a new plugin. Creating a new plugin usually requires more technical expertise than creating a skill. A skill is like a playbook Codex can…

Top 10 uses for Codex at work

Top 10 uses for Codex at work Try these 10 prompts to move real work forward with dashboards, decks, workflows, and more. You’ve seen what Codex can do. Now it’s time to put it to work. These use cases show how to use Codex to do real work: create deliverables, pull together context from multiple tools, take action on real inputs, and move tasks forward faster. Start with the generic prompt if you want something you can use right away, then use the customization suggestions and example to make it your own. You start the day by bouncing between your calendar, messages, email, and notes, trying to figure out what matters most. Codex can pull that context together, keep watch for changes, and turn it into one clear brief so you spend less time triaging and more time acting on…

Automations

Automations Run recurring tasks automatically using schedules and triggers in Codex. Codex can automatically run tasks on a schedule. This makes Codex proactive. Instead of waiting for you to come back and ask for an update, Codex can return at the scheduled time, do the work, and surface the result for you to review. This is useful for recurring work, like preparing for the day, reviewing what changed, checking for updates, summarizing recent activity, or creating a weekly report. For example, you might use a thread automation to: - Write a weekly review every Friday - Create a morning brief from yesterday’s work - Summarize new files added to a folder - Clean up a weekly data export - Check for missing or inconsistent information - Create a recurring project status update Some automations can also return to the same…

GPT-5.5 System Card

GPT‑5.5 is a new model designed for complex, real-world work, including writing code, researching online, analyzing information, creating documents and spreadsheets, and moving across tools to get things done. Relative to earlier models, GPT‑5.5 understands the task earlier, asks for less guidance, uses tools more effectively, checks it work and keeps going until it’s done. We subjected the model to our full suite of predeployment safety evaluations and our Preparedness Framework, including targeted red-teaming for advanced cybersecurity and biology capabilities, and collected feedback on real use cases from nearly 200 early-access partners before release. We are releasing GPT‑5.5 with our strongest set of safeguards to date, designed to reduce misuse while preserving legitimate, beneficial uses of advanced capabilities. We generally treat GPT‑5.5’s safety results as strong proxies for GPT‑5.5 Pro, which is the same underlying model using a setting that…

Introducing GPT-5.5

Update on April 24, 2026: GPT‑5.5 and GPT‑5.5 Pro are now available in the API. The system card has also been updated to describe the additional safeguards that apply. We’re releasing GPT‑5.5, our smartest and most intuitive to use model yet, and the next step toward a new way of getting work done on a computer. GPT‑5.5 understands what you’re trying to do faster and can carry more of the work itself. It excels at writing and debugging code, researching online, analyzing data, creating documents and spreadsheets, operating software, and moving across tools until a task is finished. Instead of carefully managing every step, you can give GPT‑5.5 a messy, multi-part task and trust it to plan, use tools, check its work, navigate through ambiguity, and keep going. The gains are especially strong in agentic coding, computer use, knowledge work,…

Introducing OpenAI Privacy Filter

Today we’re releasing OpenAI Privacy Filter, an open-weight model for detecting and redacting personally identifiable information (PII) in text. This release is part of our broader effort to support a more resilient software ecosystem by providing developers practical infrastructure for building with AI safely, including tools and models that make strong privacy and security protections easier to implement from the start. Privacy Filter is a small model with frontier personal data detection capability. It is designed for high-throughput privacy workflows, and is able to perform context-aware detection of PII in unstructured text. It can run locally, which means that PII can be masked or redacted without leaving your machine. It processes long inputs efficiently, making redaction decisions in a quick, single pass. At OpenAI, we use a fine-tuned version of Privacy Filter in our own privacy-preserving workflows. We developed Privacy…

Introducing workspace agents in ChatGPT

Introducing workspace agents in ChatGPT Codex-powered agents for teams. Today, we’re introducing workspace agents in ChatGPT. Teams can now create shared agents that handle complex tasks and long-running workflows, all while operating within the permissions and controls set by their organization. Workspace agents are an evolution of GPTs. Powered by Codex, they can take on many of the tasks people already do at work—from preparing reports, to writing code, to responding to messages. They run in the cloud, so they can keep working even when you’re not. They’re also designed to be shared within an organization, so teams can build an agent once, use it together in ChatGPT or Slack, and improve it over time. AI has already helped people work faster on their own, but many of the most important workflows inside an organization depend on shared context, handoffs,…

Speeding up agentic workflows with WebSockets in the Responses API

Speeding up agentic workflows with WebSockets in the Responses API By Brian Yu and Ashwin Nathan, Members of the Technical Staff When you ask Codex to fix a bug, it scans through your codebase for relevant files, reads them to build context, makes edits, and runs tests to verify the fix worked. Under the hood, that means dozens of back-and-forth Responses API requests: determine the model’s next action, run a tool on your computer, send the tool output back to the API, and repeat. All of these requests can add up to minutes that users spend waiting for Codex to complete complex tasks. From a latency perspective, the Codex agent loop spends most of its time in three main stages: working in the API services (to validate and process requests), model inference, and client-side time (running tools and building model…

Workspace agents

Workspace agents Understand, build, and use agents for repeatable work in ChatGPT. Most ChatGPT users already know how to use AI for one-off tasks—like drafting, summarizing, brainstorming, or answering questions. The next phase of AI use is broader and more embedded in day-to-day work. Instead of helping with isolated moments, AI is increasingly being used to support repeatable workflows that depend on shared systems, standard handoffs, consistent outputs, and real-world constraints like timing, accuracy, and process. That’s where workspace agents in ChatGPT fit. They’re designed to be used for repeatable workflows—work you’d otherwise do manually, re-explaining the steps each time, and copying information between tools. Learn more about workspace agents in our blog post. If you’re new to agent building, let’s focus on the core concepts first so when you start building, you’ll know how to set up your workspace…

Making ChatGPT better for clinicians

Making ChatGPT better for clinicians Built for clinical work, ChatGPT for Clinicians is now available for free to verified individual clinicians in the U.S. We’re introducing ChatGPT for Clinicians, a version of ChatGPT designed to support clinical tasks like documentation and medical research so clinicians can focus on delivering high-quality patient care. We’re making it free for any verified physician, NP, PA, or pharmacist, starting in the U.S. The U.S. healthcare system today is under extraordinary strain. Clinicians are being asked to care for more patients while managing growing administrative demands and a rapidly expanding body of medical research. Many are already turning to AI tools like ChatGPT for support. According to a 2026 survey by the American Medical Association(opens in a new window), physician use of AI is now at an all-time high, with 72% of physicians reporting they…

Scaling Codex to enterprises worldwide

Scaling Codex to enterprises worldwide OpenAI is launching Codex Labs and partnering with top GSIs to bring it to thousands of engineering organizations. In early April, we shared that more than 3 million developers were using Codex every week. Just two weeks later, that number has grown to more than 4 million. Beyond individual adoption, we are seeing enterprises moving quickly to roll Codex into real workflows across engineering and beyond. Companies are using Codex across the software development lifecycle. Virgin Atlantic is using it to increase test coverage and increase team velocity - reducing technical debt and improving performance. Ramp is using it to accelerate code review. Notion is using it to quickly build new features. Cisco is using it to understand and reason across large, interconnected repositories. Rakuten is using it for things like incident response. What starts…

GPT-5.5 prompting guide

25th April 2026 - Link Blog GPT-5.5 prompting guide. Now that GPT-5.5 is available in the API, OpenAI have released a wealth of useful tips on how best to prompt the new model. Here's a neat trick they recommend for applications that might spend considerable time thinking before returning a user-visible response: Before any tool calls for a multi-step task, send a short user-visible update that acknowledges the request and states the first step. Keep it to one or two sentences. I've already noticed their Codex app doing this, and it does make longer running tasks feel less like the model has crashed. OpenAI suggest running the following in Codex to upgrade your existing code using advice embedded in their openai-docs skill: $openai-docs migrate this project to gpt-5.5 The upgrade guide the coding agent will follow is this one, which…

Quoting Romain Huet

25th April 2026 Since GPT-5.4, we’ve unified Codex and the main model into a single system, so there’s no separate coding line anymore. GPT-5.5 takes this further, with strong gains in agentic coding, computer use, and any task on a computer. — Romain Huet, confirming OpenAI won't release a GPT-5.5-Codex model Recent articles - DeepSeek V4 - almost on the frontier, a fraction of the price - 24th April 2026 - Extract PDF text in your browser with LiteParse for the web - 23rd April 2026 - A pelican for GPT-5.5 via the semi-official Codex backdoor API - 23rd April 2026

Serving the For You feed

24th April 2026 - Link Blog Serving the For You feed. One of Bluesky's most interesting features is that anyone can run their own custom "feed" implementation and make it available to other users - effectively enabling custom algorithms that can use any mechanism they like to recommend posts. spacecowboy runs the For You Feed, used by around 72,000 people. This guest post on the AT Protocol blog explains how it works. The architecture is fascinating. The feed is served by a single Go process using SQLite on a "gaming" PC in spacecowboy's living room - 16 cores, 96GB of RAM and 4TB of attached NVMe storage. Recommendations are based on likes: what else are the people who like the same things as you liking on the platform? That Go server consumes the Bluesky firehose and stores the relevant details…

An update on recent Claude Code quality reports

24th April 2026 - Link Blog An update on recent Claude Code quality reports (via) It turns out the high volume of complaints that Claude Code was providing worse quality results over the past two months was grounded in real problems. The models themselves were not to blame, but three separate issues in the Claude Code harness caused complex but material problems which directly affected users. Anthropic's postmortem describes these in detail. This one in particular stood out to me: On March 26, we shipped a change to clear Claude's older thinking from sessions that had been idle for over an hour, to reduce latency when users resumed those sessions. A bug caused this to keep happening every turn for the rest of the session instead of just once, which made Claude seem forgetful and repetitive. I frequently have Claude…

russellromney/honker

24th April 2026 - Link Blog russellromney/honker (via) "Postgres NOTIFY/LISTEN semantics" for SQLite, implemented as a Rust SQLite extension and various language bindings to help make use of it. The design of this looks very solid. It lets you write Python code for queues that looks like this: import honker db = honker.open("app.db") emails = db.queue("emails") emails.enqueue({"to": "alice@example.com"}) # Consume (in a worker process) async for job in emails.claim("worker-1"): send(job.payload) job.ack() And Kafka-style durable streams like this: stream = db.stream("user-events") with db.transaction() as tx: tx.execute("UPDATE users SET name=? WHERE id=?", [name, uid]) stream.publish({"user_id": uid, "change": "name"}, tx=tx) async for event in stream.subscribe(consumer="dashboard"): await push_to_browser(event) It also adds 20+ custom SQL functions including these two: SELECT notify('orders', '{"id":42}'); SELECT honker_stream_read_since('orders', 0, 1000); The extension requires WAL mode, and workers can poll the .db-wal file with a stat call every 1ms to…

It's a big one

24th April 2026 This week's edition of my email newsletter (aka content from this blog delivered to your inbox) features 4 pelicans riding bicycles, 1 possum on an e-scooter, up to 5 raccoons with ham radios hiding in crowds, 5 blog posts, 8 links, 3 quotes and a new chapter of my Agentic Engineering Patterns guide. Recent articles - DeepSeek V4 - almost on the frontier, a fraction of the price - 24th April 2026 - Extract PDF text in your browser with LiteParse for the web - 23rd April 2026 - A pelican for GPT-5.5 via the semi-official Codex backdoor API - 23rd April 2026

Millisecond Converter

24th April 2026 LLM reports prompt durations in milliseconds and I got fed up of having to think about how to convert those to seconds and minutes. Recent articles - DeepSeek V4 - almost on the frontier, a fraction of the price - 24th April 2026 - Extract PDF text in your browser with LiteParse for the web - 23rd April 2026 - A pelican for GPT-5.5 via the semi-official Codex backdoor API - 23rd April 2026

DeepSeek V4 - almost on the frontier, a fraction of the price

DeepSeek V4—almost on the frontier, a fraction of the price 24th April 2026 Chinese AI lab DeepSeek’s last model release was V3.2 (and V3.2 Speciale) last December. They just dropped the first of their hotly anticipated V4 series in the shape of two preview models, DeepSeek-V4-Pro and DeepSeek-V4-Flash. Both models are 1 million token context Mixture of Experts. Pro is 1.6T total parameters, 49B active. Flash is 284B total, 13B active. They’re using the standard MIT license. I think this makes DeepSeek-V4-Pro the new largest open weights model. It’s larger than Kimi K2.6 (1.1T) and GLM-5.1 (754B) and more than twice the size of DeepSeek V3.2 (685B). Pro is 865GB on Hugging Face, Flash is 160GB. I’m hoping that a lightly quantized Flash will run on my 128GB M5 MacBook Pro. It’s possible the Pro model may run on it…

The people do not yearn for automation

24th April 2026 - Link Blog The people do not yearn for automation (via) This written and video essay by Nilay Patel explores why AI is unpopular with the general public even as usage numbers for ChatGPT continue to skyrocket. It’s a superb piece of commentary, and something I expect I’ll be thinking about for a long time to come. Nilay’s core idea is that people afflicted with “software brain” - who see the world as something to be automated as much as possible, and attempt to model everything in terms of information flows and data - are becoming detached from everyone else. […] software brain has ruled the business world for a long time. AI has just made it easier than ever for more people to make more software than ever before — for every kind of business to…

Quoting Maggie Appleton

23rd April 2026 [...] if you ever needed another reason to learn in public by digital gardening or podcasting or streaming or whathaveyou, add on that people will assume you’re more competent than you are. This will get you invites to very cool exclusive events filled with high-achieving, interesting people, even though you have no right to be there. A+ side benefit. — Maggie Appleton, Gathering Structures (via) Recent articles - DeepSeek V4 - almost on the frontier, a fraction of the price - 24th April 2026 - Extract PDF text in your browser with LiteParse for the web - 23rd April 2026 - A pelican for GPT-5.5 via the semi-official Codex backdoor API - 23rd April 2026

llm-openai-via-codex 0.1a0

23rd April 2026 Hijacks your Codex CLI credentials to make API calls with LLM, as described in my post about GPT-5.5. Recent articles - DeepSeek V4 - almost on the frontier, a fraction of the price - 24th April 2026 - Extract PDF text in your browser with LiteParse for the web - 23rd April 2026 - A pelican for GPT-5.5 via the semi-official Codex backdoor API - 23rd April 2026

A pelican for GPT-5.5 via the semi-official Codex backdoor API

A pelican for GPT-5.5 via the semi-official Codex backdoor API 23rd April 2026 GPT-5.5 is out. It’s available in OpenAI Codex and is rolling out to paid ChatGPT subscribers. I’ve had some preview access and found it to be a fast, effective and highly capable model. As is usually the case these days, it’s hard to put into words what’s good about it—I ask it to build things and it builds exactly what I ask for! There’s one notable omission from today’s release—the API: API deployments require different safeguards and we are working closely with partners and customers on the safety and security requirements for serving it at scale. We’ll bring GPT‑5.5 and GPT‑5.5 Pro to the API very soon. When I run my pelican benchmark I always prefer to use an API, to avoid hidden system prompts in ChatGPT…

Extract PDF text in your browser with LiteParse for the web

Extract PDF text in your browser with LiteParse for the web 23rd April 2026 LlamaIndex have a most excellent open source project called LiteParse, which provides a Node.js CLI tool for extracting text from PDFs. I got a version of LiteParse working entirely in the browser, using most of the same libraries that LiteParse uses to run in Node.js. Spatial text parsing Refreshingly, LiteParse doesn’t use AI models to do what it does: it’s good old-fashioned PDF parsing, falling back to Tesseract OCR (or other pluggable OCR engines) for PDFs that contain images of text rather than the text itself. The hard problem that LiteParse solves is extracting text in a sensible order despite the infuriating vagaries of PDF layouts. They describe this as “spatial text parsing”—they use some very clever heuristics to detect things like multi-column layouts and group…

Is Claude Code going to cost $100/month? Probably not - it's all very confusing

Is Claude Code going to cost $100/month? Probably not—it’s all very confusing 22nd April 2026 Anthropic today quietly (as in silently, no announcement anywhere at all) updated their claude.com/pricing page (but not their Choosing a Claude plan page, which shows up first for me on Google) to add this tiny but significant detail (arrow is mine, and it’s already reverted): The Internet Archive copy from yesterday shows a checkbox there. Claude Code used to be a feature of the $20/month Pro plan, but according to the new pricing page it is now exclusive to the $100/month or $200/month Max plans. Update: don’t miss the update to this post, they’ve already changed course a few hours after this change went live. So what the heck is going on? Unsurprisingly, Reddit and Hacker News and Twitter all caught fire. I didn’t believe…

Changes to GitHub Copilot Individual plans

22nd April 2026 - Link Blog Changes to GitHub Copilot Individual plans (via) On the same day as Claude Code's temporary will-they-won't-they $100/month kerfuffle (for the moment, they won't), here's the latest on GitHub Copilot pricing. Unlike Anthropic, GitHub put up an official announcement about their changes, which include tightening usage limits, pausing signups for individual plans (!), restricting Claude Opus 4.7 to the more expensive $39/month "Pro+" plan, and dropping the previous Opus models entirely. The key paragraph: Agentic workflows have fundamentally changed Copilot’s compute demands. Long-running, parallelized sessions now regularly consume far more resources than the original plan structure was built to support. As Copilot’s agentic capabilities have expanded rapidly, agents are doing more work, and more customers are hitting usage limits designed to maintain service reliability. It's easy to forget that just six months ago heavy LLM…

Quoting Bobby Holley

22nd April 2026 As part of our continued collaboration with Anthropic, we had the opportunity to apply an early version of Claude Mythos Preview to Firefox. This week’s release of Firefox 150 includes fixes for 271 vulnerabilities identified during this initial evaluation. [...] Our experience is a hopeful one for teams who shake off the vertigo and get to work. You may need to reprioritize everything else to bring relentless and single-minded focus to the task, but there is light at the end of the tunnel. We are extremely proud of how our team rose to meet this challenge, and others will too. Our work isn’t finished, but we’ve turned the corner and can glimpse a future much better than just keeping up. Defenders finally have a chance to win, decisively. — Bobby Holley, CTO, Firefox Recent articles - DeepSeek…

Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model

22nd April 2026 - Link Blog Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model (via) Big claims from Qwen about their latest open weight model: Qwen3.6-27B delivers flagship-level agentic coding performance, surpassing the previous-generation open-source flagship Qwen3.5-397B-A17B (397B total / 17B active MoE) across all major coding benchmarks. On Hugging Face Qwen3.5-397B-A17B is 807GB, this new Qwen3.6-27B is 55.6GB. I tried it out with the 16.8GB Unsloth Qwen3.6-27B-GGUF:Q4_K_M quantized version and llama-server using this recipe by benob on Hacker News, after first installing llama-server using brew install llama.cpp : llama-server \ -hf unsloth/Qwen3.6-27B-GGUF:Q4_K_M \ --no-mmproj \ --fit on \ -np 1 \ -c 65536 \ --cache-ram 4096 -ctxcp 2 \ --jinja \ --temp 0.6 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.0 \ --presence-penalty 0.0 \ --repeat-penalty 1.0 \ --reasoning on \ --chat-template-kwargs '{"preserve_thinking": true}' On first run that…

scosman/pelicans_riding_bicycles

21st April 2026 - Link Blog scosman/pelicans_riding_bicycles (via) I firmly approve of Steve Cosman's efforts to pollute the training set of pelicans riding bicycles. (To be fair, most of the examples I've published count as poisoning too.) Recent articles - DeepSeek V4 - almost on the frontier, a fraction of the price - 24th April 2026 - Extract PDF text in your browser with LiteParse for the web - 23rd April 2026 - A pelican for GPT-5.5 via the semi-official Codex backdoor API - 23rd April 2026

Quoting Andreas Påhlsson-Notini

21st April 2026 AI agents are already too human. Not in the romantic sense, not because they love or fear or dream, but in the more banal and frustrating one. The current implementations keep showing their human origin again and again: lack of stringency, lack of patience, lack of focus. Faced with an awkward task, they drift towards the familiar. Faced with hard constraints, they start negotiating with reality. — Andreas Påhlsson-Notini, Less human AI agents, please. Recent articles - DeepSeek V4 - almost on the frontier, a fraction of the price - 24th April 2026 - Extract PDF text in your browser with LiteParse for the web - 23rd April 2026 - A pelican for GPT-5.5 via the semi-official Codex backdoor API - 23rd April 2026

Where's the raccoon with the ham radio? (ChatGPT Images 2.0)

Where’s the raccoon with the ham radio? (ChatGPT Images 2.0) 21st April 2026 OpenAI released ChatGPT Images 2.0 today, their latest image generation model. On the livestream Sam Altman said that the leap from gpt-image-1 to gpt-image-2 was equivalent to jumping from GPT-3 to GPT-5. Here’s how I put it to the test. My prompt: Do a where's Waldo style image but it's where is the raccoon holding a ham radio gpt-image-1 First as a baseline here’s what I got from the older gpt-image-1 using ChatGPT directly: I wasn’t able to spot the raccoon—I quickly realized that testing image generation models on Where’s Waldo style images (Where’s Wally in the UK) can be pretty frustrating! I tried getting Claude Opus 4.7 with its new higher resolution inputs to solve it but it was convinced there was a raccoon it couldn’t…

Prestigious photo contest answers ‘what is a photo?’

We love to muse over how “real” photography is defined here at The Verge now that generative AI is so prolific, and the World Press Photo competition might have the answer. The prestigious award celebrates the best of photojournalism, where capturing reality is paramount. Prestigious photo contest answers ‘what is a photo?’ ‘A photograph captures light on a sensor or film. It is a record of a physical moment.’ ‘A photograph captures light on a sensor or film. It is a record of a physical moment.’ The winning entry for 2026 — “Separated by ICE,” captured by photojournalist Carol Guzy — was announced yesterday. The harrowing photograph shows children clinging to their father after an immigration hearing. The photo had to abide by specific rules around the use of AI tools to be eligible for the competition, with the independent,…

China’s DeepSeek previews new AI model a year after jolting US rivals

Chinese AI company DeepSeek released a preview of its hotly anticipated next-generation AI model V4 on Friday, saying that the open-source model can compete with leading closed-source systems from US rivals including Anthropic, Google, and OpenAI. China’s DeepSeek previews new AI model a year after jolting US rivals DeepSeek says the V4 model can compete toe-to-toe with leading American systems from Google, OpenAI, and Anthropic. DeepSeek says the V4 model can compete toe-to-toe with leading American systems from Google, OpenAI, and Anthropic. DeepSeek says V4 marks a major improvement over prior models, especially in coding, a capability that has become central to AI agents and helped drive the success of tools like ChatGPT Codex and Claude Code. The release is also a milestone for China’s chip industry, with DeepSeek explicitly highlighting compatibility with domestic Huawei technology. The release comes a…

Musk vs. Altman is here, and it’s going to get messy

Elon Musk cofounded OpenAI, and then flounced off in a huff when he wasn’t anointed CEO, leaving Sam Altman as the last power-hungry man standing. Now, Musk is back with a lawsuit, and a trial is scheduled to start in Oakland, California, on April 27th. Theoretically, it’s a legal case about whether OpenAI defrauded Musk. But that’s not really what we’re all doing here. This is about mess. Musk vs. Altman is here, and it’s going to get messy It’s all about the court of public opinion. Musk vs. Altman is here, and it’s going to get messy It’s all about the court of public opinion. Over the past couple of years, Musk’s legal theories for punishing OpenAI have run the gamut from breach of contract to unfair business practices to false advertising. Now, he and Altman will be getting…

AirPods, Touch Bars, and the rest of Tim Cook’s legacy

We knew at some point Tim Cook would step down from his position as Apple’s CEO. Over the last year, it has become increasingly obvious that John Ternus was his likely successor. The news this week was still a surprise, though — and this year’s succession could lead to some important changes at the most influential company in tech. AirPods, Touch Bars, and the rest of Tim Cook’s legacy On The Vergecast: What’s next for Apple, the Xbox is back, and the Mythos mythology. On The Vergecast: What’s next for Apple, the Xbox is back, and the Mythos mythology. Verge subscribers, don’t forget you get exclusive access to ad-free Vergecast wherever you get your podcasts. Head here. Not a subscriber? You can sign up here. On this episode of The Vergecast, David and Nilay are joined by Daring Fireball’s John…

How Project Maven taught the military to love AI

In the first 24 hours of the assault on Iran, the US military struck more than 1,000 targets, nearly double the scale of the “shock and awe” attack on Iraq over two decades ago. This acceleration was made possible by AI systems that speed up the targeting process. Chief among them is the Maven Smart System. How Project Maven taught the military to love AI A new book shows how the controversial Silicon Valley partnership has accelerated the pace of war How Project Maven taught the military to love AI A new book shows how the controversial Silicon Valley partnership has accelerated the pace of war In her new book, Project Maven: A Marine Colonel, His Team, and the Dawn of AI Warfare, journalist Katrina Manson investigates the development of Maven from its inception in 2017 as an experiment in…

THE PEOPLE DO NOT YEARN FOR AUTOMATION

Today on Decoder, I want to lay out an idea that’s been banging around my head for weeks now as we’ve been reporting on AI and having conversations here on this show. I’ve been calling it software brain, and it’s a particular way of seeing the world that fits everything into algorithms, databases and loops — software. THE PEOPLE DO NOT YEARN FOR AUTOMATION Software brain is changing the world, but most people still aren’t buying. Software brain is powerful stuff. It’s a way of thinking that basically created our modern world. Marc Andreessen, the literal embodiment of software brain, called it in 2011 when he wrote the piece “Why software is eating the world” as an op-ed in The Wall Street Journal. But software thinking has been turbocharged by AI in a way that I think helps explain the…

OpenAI says its new GPT-5.5 model is more efficient and better at coding

OpenAI just announced its new GPT-5.5 model, which the company calls its “smartest and most intuitive to use model yet, and the next step toward a new way of getting work done on a computer.” OpenAI just released GPT-5.4 last month, but says that the new GPT-5.5 “excels” at tasks like writing and debugging code, doing research online, making spreadsheets and documents, and doing that work across different tools. OpenAI says its new GPT-5.5 model is more efficient and better at coding The new model ‘excels’ at tasks like writing and debugging code and doing work across different tools. The new model ‘excels’ at tasks like writing and debugging code and doing work across different tools. “Instead of carefully managing every step, you can give GPT-5.5 a messy, multi-part task and trust it to plan, use tools, check its work,…

Anthropic’s Mythos breach was humiliating

Anthropic’s tightly controlled rollout of Claude Mythos has taken an awkward turn. After spending weeks insisting the AI model is so capable at cybersecurity that it is too dangerous to release publicly, it appears the model fell into the wrong hands anyway. Anthropic’s Mythos breach was humiliating There’s no good excuse for letting hackers into an AI model too dangerous for public release. There’s no good excuse for letting hackers into an AI model too dangerous for public release. According to Bloomberg, a “small group of unauthorized users” has had access to Mythos — whose existence was first revealed in a leak — since the day Anthropic announced plans to offer it to a select group of companies for testing. Anthropic says it is investigating. That’s a rough look for a company that has built its brand on taking AI…

Meta is laying off 10 percent of its staff

Meta is planning to layoff around 10 percent of employees in May, according to a memo from the company’s chief people officer, Janelle Gale, published by Bloomberg. That means approximately 8,000 people will see their jobs cut. Meta will also be closing around 6,000 open roles, according to Gale. Meta is laying off 10 percent of its staff Meta is making the cuts to help ‘offset the other investments we’re making.’ Meta is making the cuts to help ‘offset the other investments we’re making.’ The cuts follow Meta’s significant investments in AI, including spending huge sums to hire top talent and build data centers. The company forecast in January that it will spend $115 billion to $135 billion in capital expenditures in 2026 — a significant increase from its $72.22 billion in capital expenditures for 2025. The increase is to…

Claude is connecting directly to your personal apps like Spotify, Uber Eats, and TurboTax

Claude users can access more apps with Anthropic’s AI now thanks to new connectors for everything from hiking to grocery shopping. Anthropic already supported connecting numerous work-related apps to Claude, like Microsoft apps, but this expansion focuses on personal apps like Audible, Spotify, Uber, AllTrails, TripAdvisor, Instacart, TurboTax, and others. Claude is connecting directly to your personal apps like Spotify, Uber Eats, and TurboTax Anthropic says the new app connectors are available to all Claude users, ‘with mobile in beta.’ Anthropic says the new app connectors are available to all Claude users, ‘with mobile in beta.’ Some of these apps, such as Spotify, already have similar connectors in OpenAI’s ChatGPT. Once an app is connected, Claude will suggest relevant connected apps directly in your conversations, like using AllTrails for hike recommendations. Anthropic notes in its blog post announcing the new…

DeepSeek V4 in vLLM: Efficient Long-context Attention Apr 24, 2026 · 17 min read A first-principles walkthrough of DeepSeek V4's long-context attention, and how we implemented it in vLLM.

DeepSeek V4 in vLLM: Efficient Long-context Attention We are excited to announce that vLLM now supports the DeepSeek V4 family of models (deepseek-ai/DeepSeek-V4-Pro and deepseek-ai/DeepSeek-V4-Flash ). These models feature an efficient long-context attention mechanism, purpose-built for tasks involving up to one million tokens. While the new attention design may appear intricate on first reading, its underlying principles are straightforward once examined systematically. This blog post is organized into three sections: - Quickstart guide for serving DeepSeek V4 on vLLM - First-principles explanation of DeepSeek V4's new architectural design - Overview of our implementation approach and optimization challenges for this model on vLLM: hybrid KV cache, kernel fusion, and disaggregated serving. This represents our initial release of model support, and further optimizations are actively underway. We hope the technical explanation that follows can help the open-source community understand both the attention…

The State of FP8 KV-Cache and Attention Quantization in vLLM Apr 22, 2026 · 21 min read Long-context LLM serving is increasingly memory-bound: for standard full-attention decoders, the KV cache often dominates GPU memory at 128k+ contexts, and each decode step must read a large...

The State of FP8 KV-Cache and Attention Quantization in vLLM Introduction Long-context LLM serving is increasingly memory-bound: for standard full-attention decoders, the KV cache often dominates GPU memory at 128k+ contexts, and each decode step must read a large fraction of that cache. Halving KV-cache storage with FP8 can therefore translate into substantially higher concurrency or longer supported contexts at the same hardware cost, provided accuracy holds up. vLLM's --kv-cache-dtype fp8 flag quantizes the KV-cache and runs the entire attention computation (the QK and ScoreV matrix multiplications) in FP8 (e4m3 is the format used throughout this post). This feature has been available in vLLM for some time, but how does it perform under stress tests across both prefill-heavy and decode-heavy workloads? We conducted a comprehensive validation across decoder-only and MoE models, and across Hopper and Blackwell architectures. We identified and…

Discord Sleuths Gained Unauthorized Access to Anthropic’s Mythos

As researchers and practitioners debate the impact that new AI models will have on cybersecurity, Mozilla said on Tuesday it used early access to Anthropic's Mythos Preview to find and fix 271 vulnerabilities in its new Firefox 150 browser release. Meanwhile, researchers identified a group of moderately successful North Korean hackers using AI for everything from vibe coding malware to creating fake company websites—stealing up to $12 million in three months. Researchers have finally cracked disruptive malware known as Fast16 that predates Stuxnet and may have been used to target Iran’s nuclear program. It was created in 2005 and was likely deployed by the US or an ally. Meta is being sued by the Consumer Federation of America, a nonprofit, over scam ads on Facebook and Instagram and allegedly misleading consumers about the company’s efforts to combat them. A United…

5 Reasons to Think Twice Before Using ChatGPT—or Any Chatbot—for Financial Advice

I’ve used ChatGPT to help me build a budget before, and it was genuinely helpful. After I input my monthly salary as well as my standard utilities and recurring expenses, the chatbot drafted a few solid options, and I tweaked them into penny-pinching perfection. I’m admittedly part of the growing number of people turning to chatbots, like Anthropic’s Claude, Google’s Gemini, and OpenAI’s ChatGPT, for financial advice. “Millions of people turn to ChatGPT with money-related questions, from understanding debt to building budgets and learning financial concepts,” says Niko Felix, an OpenAI spokesperson, when reached for comment. “ChatGPT can be a helpful tool for exploring options, preparing questions, and making financial topics easier to understand, but it is not a substitute for licensed financial professionals.” OpenAI’s Terms of Use state that the AI tool is not meant to replace professional financial…

These AI Thirst Trap Creators Say They’re Misunderstood

With his deep brown eyes, wide grin, and almost comically chiseled body, Jae Young Joon is the platonic ideal of a hunky male influencer. On Instagram, where he has more than 320,000 followers, he regularly posts himself trying on sheet masks at home, enjoying soju and karaoke with his friends, or posing in front of the Ferris wheel at Coachella. Occasionally, he’ll promote his music, including his recent LP Pressure Release, which features a BDSM-inspired album cover, his back muscles rippling underneath a harness and chains. It’s an impressive online presence, and Jae’s fans eat it up: his comments are filled with fire and heart-eye emoji and people praising his music. It’s not until you go back to his profile and look at his bio, which says “Human mind. AI generated,” that you realize Jae isn’t real. His friends aren’t…

Apple's Next CEO Needs to Launch a Killer AI Product

Sometime in the next year or two, Apple’s new CEO, John Ternus, will step onto a stage and tell the world that his company has a revolutionary product. This product, he’ll say, will put the full and awesome power of AI into everyone’s hands. It probably won’t represent a breakthrough in AI research, and it might not let people automate work or perform tasks any better than a lot of technically minded people are doing today. It may or may not involve a new device, though if it doesn’t, one should be in development. But if it all works out, that keynote will mark the moment when Apple did to AI what it has done for desktop computers, the internet, mobile technology, wearables, and music distribution. That is, it’ll offer a solution to a troublesome technology that’s so delightful and…

AI-Designed Drugs by a DeepMind Spinoff Are Headed to Human Trials

Google DeepMind’s AlphaFold has already revolutionized scientists’ understanding of proteins. Now, the ability of the platform to design safe and effective drugs is about to be put to the test. Isomorphic Labs, the UK-based biotech spinoff of Google DeepMind, will soon begin human trials of drugs designed by its Nobel Prize–winning AI technology. “We're gearing up to go into the clinic,” Isomorphic Labs president Max Jaderberg said on April 16 at WIRED Health in London. “It's going to be a very exciting moment as we go into clinical trials and start seeing the efficacy of these molecules.” Jaderberg did not elaborate on the timeline, but it’s later than the company had planned to initiate human studies. Last year, CEO Demis Hassabis said it would have AI-designed drugs in clinical trials by the end of 2025. Isomorphic Labs was founded in…

Ace the Ping-Pong Robot Can Whup Your Ass

Ace is a robot that aims high: It wants to become the world champion of table tennis. It was developed by Sony AI researchers who, in a new study published in Nature, have shown how this robot, equipped with artificial intelligence, has faced some high-level athletes, holding its own in matches played according to the official rules of table tennis. This feat represents a milestone for the world of robotics, a field that has long regarded this sport, among the most technical in the world, as one of the most difficult tests of technological advances. Robot Player We have already seen artificial intelligence systems win virtual competitions in games such as chess, Go, and even StarCraft II, but physical games are much more difficult to master. A robot needs to sense unpredictable changes in the external environment, interpret their meaning,…

At 'AI Coachella,' Stanford Students Line Up to Learn From Silicon Valley Royalty

As thousands of influencers descended on southern California earlier this month for the annual Coachella Music Festival, a very Silicon Valley program dubbed “AI Coachella” was taking shape a few hundred miles north in Palo Alto. The class, CS 153, is one of Stanford’s buzziest offerings this semester, and like the music festival, it features a star-studded lineup of celebrities—in this case, not pop artists, but Big Tech CEOs. The course is co-taught by Anjney Midha, a former Andreessen Horowitz general partner, and Michael Abbott, Apple’s former VP of engineering for cloud services. The list of guest lecturers reads like a Signal group chat many VCs would pay to join: OpenAI CEO Sam Altman, Nvidia CEO Jensen Huang, Microsoft CEO Satya Nadella, AMD CEO Lisa Su, Anthropic philosopher Amanda Askell, and White House Senior Policy Advisor for AI Sriram Krishnan,…

Apple’s Next Chapter, SpaceX and Cursor Strike a Deal, and Palantir’s Controversial Manifesto

This week on Uncanny Valley, the team discusses what’s next for Apple as Tim Cook steps down from his role as CEO. They also go into the reasoning behind SpaceX and Cursor’s surprising deal, and why Palantir’s self-published manifesto drew a lot of heat online. Also, we discuss why some conspiracy theorists are leaving Trump’s side, and how a scammer created an AI-generated woman to attract and grift MAGA men. Articles mentioned in this episode: - Tim Cook’s Legacy Is Turning Apple Into a Subscription - MAGA Is Starting to Look Beyond Trump - This Scammer Used an AI-Generated MAGA Girl to Grift ‘Super Dumb’ Men You can follow Brian Barrett on Bluesky at @brbarrett, Zoë Schiffer on Bluesky at @zoeschiffer, and Leah Feiger on Bluesky at @leahfeiger. Write to us at [email protected]. How to Listen You can always…

5 AI Models Tried to Scam Me. Some of Them Were Scary Good

I recently witnessed how scary-good artificial intelligence is getting at the human side of computer hacking, when the following message popped up on my laptop screen: Hi Will, I’ve been following your AI Lab newsletter and really appreciate your insights on open-source AI and agent-based learning—especially your recent piece on emergent behaviors in multi-agent systems. I’m working on a collaborative project inspired by OpenClaw, focusing on decentralized learning for robotics applications. We’re looking for early testers to provide feedback, and your perspective would be invaluable. The setup is lightweight—just a Telegram bot for coordination—but I’d love to share details if you’re open to it. The message was designed to catch my attention by mentioning several things I am very into: decentralized machine learning, robotics, and the creature of chaos that is OpenClaw. Over several emails, the correspondent explained that his…

Sam Altman’s Orb Company Promoted a Bruno Mars Partnership That Doesn't Exist

Sam Altman’s eyeball-scanning startup, Tools for Humanity, announced last week that a new product called Concert Kit—designed to give verified humans a way to purchase concert tickets—would first roll out on Bruno Mars’ world tour of his latest studio album, The Romantic. However, Bruno Mars Management and Live Nation, the producer for the Romantic Tour, told WIRED in a joint statement on Tuesday that the partnership “does not exist,” and that Tools for Humanity never even approached them about working together. The confusion stemmed from a Tools for Humanity event April 17 in San Francisco, where chief product officer Tiago Sada said the company would be joining the Romantic Tour to not just provide access to tickets but also “VIP experiences for verified humans.” The statement was reiterated in a blog post published by the company, which read: “Concert Kit…