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TechCrunch AI·just now

Final 24 hours to save up to $410 on your TechCrunch Disrupt 2026 ticket

You now have until tonight at 11:59 p.m. PT to lock in Early Bird savings of up to $410 for TechCrunch Disrupt 2026 before prices increase. Join 10,000+ tech leaders in October for one of the most anticipated tech events of the year. Register now.

TechCrunch AI·just now

Jony Ive’s funky Ferrari

Most people will never own, drive, or even sit inside a Ferrari Luce. (If you can, or do… hit us up.) There's still no question that Ferrari's first electric vehicle is one of the most interesting, surprising cars of the year. With a decidedly un-Ferrari look, and lots of new technology and designs courtesy of […]

The Verge AI·just now

The Download: unlocking lithium and controlling Ebola

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. How a new extraction process could unlock the world’s lithium A new method for extracting lithium could cut costs and emissions from one of the world’s most important materials for EVs and energy storage. The technique uses a weak acid to dissolve silicate minerals. That frees not only the lithium but also other useful materials, including alumina and silica. “At scale, we believe this will be the lowest-cost way of sourcing lithium in the world,” says Ye

MIT Technology Review·just now

This chip startup just raised $135M on a bet that AI’s biggest bottleneck isn’t compute — it’s memory

South Korean chip startup XCENA is betting that AI's real bottleneck is not compute, but memory.

TechCrunch AI·just now

This AI startup will clean your home for free to train future robots

AI training startup Shift wants to clean your home for free. The catch - because, despite what its website says, there's always a catch - is that it will record cleaners as they scrub, vacuum, dust, tidy, and wash, and use that footage to train robots. Shift announced the unusual offer on social media on […]

The Verge AI·just now

The deadly Ebola outbreak is proving difficult to control

The alert was raised on May 5 . Four health-care workers in the Ituri Province of the Democratic Republic of the Congo had died from an unknown illness within four days. Rapid response teams were sent to investigate, and tests at a research center in Kinshasa revealed the culprit: the Bundibugyo virus, one of the viruses that cause Ebola. Suspected cases of the disease have snowballed in the last few weeks. By May 24, the WHO had estimated that 223 people had died from the disease. There were over 900 suspected cases. Today’s figures are likely to be higher. A couple of weeks ago, I covered th

MIT Technology Review·just now

Adobe’s conversational AI agent is a mediocre design intern

AI image tools rarely make me feel like I'm part of the creative process. They are, after all, mostly designed so that people with no design experience can type in a few words and get back a usable result. So I was pleasantly surprised by Adobe's latest take on an AI image assistant: It's a […]

The Verge AI·just now

How the Pope’s Magnifica Humanitas offers a template for individuals to meet the AI moment

Pope Leo XIV’s new encyclical on artificial intelligence includes a statement that warrants serious attention from technologists and policymakers: “Technology is never neutral.” Magnifica Humanitas (“Magnificent Humanity”) is a clarion call to all people to act with courage and solidarity as we enter an age already being transformed by artificial intelligence, the greatest change in human life since the Industrial Revolution. As the pope says, the choice before us—the choice AI presents—is one between the Tower of Babel and the rebuilding of our common humanity. In the biblical story of the T

MIT Technology Review·just now

Behavior-Induced Mirror-Prox Temporal-Difference Learning for Faster Off-Policy Prediction

arXiv:2605.28849v1 Announce Type: new Abstract: Gradient temporal-difference methods provide stable off-policy prediction with linear function approximation, but their practical performance is strongly affected by the geometry induced by the auxiliary-variable metric. Existing Mirror-Prox TD methods typically use the feature covariance metric, whereas hybrid TD methods suggest that behavior-policy transition information can provide a more informative update geometry. This paper proposes a behavior-induced Mirror-Prox temporal-difference method, called STHTD-MP, which replaces the covariance me

arXiv cs.AI·just now

Behavior-Aware Auxiliary Corrections for Off-Policy Temporal-Difference Prediction

arXiv:2605.28855v1 Announce Type: new Abstract: Temporal-difference learning with function approximation can be unstable under off-policy sampling. TDC stabilizes off-policy TD through an auxiliary covariance correction, and TDRC further regularizes this correction in a single-timescale recursion. This paper studies a behavior-aware replacement of the auxiliary covariance geometry in the linear prediction setting, which is the standard local model for understanding the feature-space dynamics of value-function approximation. We first replace the TDC auxiliary matrix (C) by the behavior Bellman

arXiv cs.AI·just now

The Cognitive Categorical Transformer: Category-Theoretic Inductive Biases for Language Modeling

arXiv:2605.28864v1 Announce Type: new Abstract: The Cognitive Categorical Transformer (CCT) is a 306M-parameter architecture that augments a pretrained GPT-2 Small backbone with cognitively grounded components derived from category theory and several inspirations from cognitive science. Under a matched-step protocol (215,000 optimizer steps, matched data, matched optimizer and schedule) on WikiText-103, CCT reaches 21.27 validation perplexity, compared with 24.19 for an identically fine-tuned GPT-2 Small baseline. The architecture therefore contributes a 2.92 PPL (12% relative) reduction beyon

arXiv cs.AI·just now

Ultra-Reduced-Impact-Encased-Logging (URIEL): propose a new method for selective sustainable logging and post-harvest silvicultural treatment in tropical forest using airborne robotics systems

arXiv:2605.28883v1 Announce Type: new Abstract: Tropical forests worldwide are under intense deforestation pressure driven by economic and political interests, and scientific evidence suggests this deforestation contributes to climate change. This paper proposes a novel logging method for tropical forests, Ultra-Reduced-Impact-Encased-Logging (URIEL). This new method is based on heli-logging techniques combined with intensive use of robotics and AI integrated with post-harvest silvicultural treatments performed by drones. The concept of appropriate equipment for this method was developed, dime

arXiv cs.AI·just now

Review Arcade: On the Human Alignment and Gameability of LLM Reviews

arXiv:2605.28897v1 Announce Type: new Abstract: LLM-generated reviews for scientific papers are gaining considerable traction and are even being officially piloted by major conferences. We have to assume that not only reviewers are using LLM-assistance, but also that authors use LLMs to revise their papers before submitting. In this work, we perform empirical experiments on papers from the 2025 ACL Rolling Review (ARR) to evaluate LLM reviews from both the author and the reviewer perspective. First, we identify a limited alignment of LLM reviews with human ones. In the best-case scenario, the

arXiv cs.AI·just now

Orthogonal Concept Erasure for Diffusion Models

arXiv:2605.28902v1 Announce Type: new Abstract: Concept erasure has emerged as a promising approach to mitigate undesired or unsafe content in diffusion models, yet existing methods still face significant limitations. While training-based methods are effective, their high computational cost limits scalability. Editing-based methods are more efficient and deployment-friendly, yet they struggle to simultaneously achieve precise concept erasure and preserve overall generative capacity. We identify this core limitation of the editing-based methods as reliance on additive parameter updates. Our emp

arXiv cs.AI·just now

Frontier LLM-based agents can overcome the ontology curation bottleneck for natural phenotypes

arXiv:2605.28965v1 Announce Type: new Abstract: Linking free-text phenotype descriptions to ontology terms, typically referred to as phenotype annotation, is essential for the cross-study integration of comparative morphological data. This labor intensive process has heavily relied on highly trained human experts, which makes it challenging to scale and thus a key bottleneck. Dahdul et al. (2018) established a Gold Standard (GS) of Entity-Quality (EQ) annotations across seven phylogenetic studies and used it to evaluate three human curators and the Semantic CharaParser NLP tool with ontology-b

arXiv cs.AI·just now

VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis

arXiv:2605.28978v1 Announce Type: new Abstract: Finite Element Analysis (FEA) serves as the cornerstone of modern engineering design. However, its workflow is inherently complex and relies heavily on domain expertise. Although recent efforts have integrated Large Language Models (LLMs) into FEA, existing approaches face limitations in handling multimodal inputs and executing complex tasks. To address these limitations, we propose VFEAgent, an end-to-end multi-agent system designed to automate FEA modeling and simulation directly from input images and problem descriptions. Our methodology integ

arXiv cs.AI·just now

BEAMS: Benchmarking and Evaluating AI for Modeling and Simulation

arXiv:2605.28994v1 Announce Type: new Abstract: AI tools to support real world decision making must be able to build simulation models that inform their recommendations and render them interpretable. Tools that can automate aspects of modeling practice must complement human expertise, not replace it. The BEAMS Initiative aims to guide the development of AI tools for modeling and simulation toward forms that are responsible and ethical by establishing benchmarks for human centered modeling and simulation practices. The initiative uses open digital and organizational infrastructure to collaborat

arXiv cs.AI·just now

Adopt $\neq$ Adapt: Longitudinal Analyses of LLM Conversations in the Wild

arXiv:2605.29018v1 Announce Type: new Abstract: Although a growing body of research has begun to describe user--LLM interactions, the picture it paints is largely static; little is known about how individual users change their behavior over time. To address this gap, we analyze the conversational trajectories of $\sim$12,000 randomly sampled Microsoft Bing Copilot users and compare these with data from WildChat-4.8M. While the Copilot data contains significant population-level trends, we find that trends in individual user trajectories are much weaker; user habits prove to be overwhelmingly st

arXiv cs.AI·just now

Funding Hot

When Models Disagree: Rethinking LLM Evaluation for Public Comment Analysis

arXiv:2605.29025v1 Announce Type: new Abstract: Federal agencies are deploying large language models (LLMs) to categorize public comment corpora, where the model's organization of the record shapes what policymakers see and which arguments register. Standard evaluation, anchored on stance accuracy against a small validated set, cannot detect when different models produce materially different categorizations of the same public input. We propose an Interpretive Audit Pipeline that treats multi-model disagreement as diagnostic of interpretive complexity and directs human review toward genuinely a

arXiv cs.AI·just now

Mind Your Tone: Does Tone Alter LLM Performance?

arXiv:2605.29027v1 Announce Type: new Abstract: The use of Large Language Models (LLMs) is proliferating, yet their performance is observed to vary based on prompting styles and tones. In this study, we investigate both whether and how tonal variations in prompts lead to disparate LLM accuracy for objective multiple-choice questions. We use two datasets: a 50-base question dataset with five tone variants and a 570-base question MMLU subset spanning 57 subjects with seven tone variants. Experiments were conducted to evaluate the performance of four cost-efficient, popular LLMs: ChatGPT-4o, Chat

arXiv cs.AI·just now

Lightweight Multimodal LLM-Enabled Cost-Effective Defect Grading of Power Transmission Equipment

arXiv:2605.28822v1 Announce Type: new Abstract: Defect grading of power transmission equipment (DGPTE) is crucial to the stability of electric energy transmission. Although existing machine learning methods exhibit strong capabilities in defect detection, they are plagued by difficulties in integrating expert experience and facing class imbalance in more refined defect grading field. To address this issue, this paper introduces a novel defect grading framework based on multimodal large language model (MLLM). Specifically, this approach maximizes the commercial MLLMs' potential of DGPTE through

arXiv cs.CL·just now

What are They Thinking? Delineation, Probing and Tracking of Concepts in LLMs

arXiv:2605.28823v1 Announce Type: new Abstract: As the influence of LLMs expands, it is imperative to gain insight into their decisions. One way to do that is to develop probes that detect the presence or absence of a broad set of concepts within the embeddings computed in an LLM - which is what we might say a model is "thinking" about. Such probes should be low-cost and easily applicable to any LLM, so that monitoring for many concepts is possible during normal operation. In this paper, we take the first steps towards developing the capability of creating many such probes by defining and exec

arXiv cs.CL·just now

A Modular Architecture for Typologically Controlled Lexicon Generation

arXiv:2605.28824v1 Announce Type: new Abstract: Constructing artificial lexicons that are pronounceable, typologically plausible, and semantically structured remains an open challenge in computational linguistics. Existing conlang generators either lack formal phonotactic guarantees or delegate generation to opaque, non-reproducible LLM-based pipelines. We propose a modular framework that samples phoneme inventories from PHOIBLE, generates word forms under interchangeable phonological grammars (deterministic, OT, and MaxEnt), and assigns meanings via a Swadesh--Leipzig--Jakarta ontology with e

arXiv cs.CL·just now

MechELK: A Mechanistic Interpretability Framework for Eliciting Latent Knowledge in Large Language Models

arXiv:2605.28825v1 Announce Type: new Abstract: Large language models (LLMs) frequently encode factual and reasoning knowledge in their internal representations that is not faithfully reflected in their surface-level outputs -- a phenomenon known as \emph{latent knowledge}. Existing approaches to eliciting latent knowledge, such as Contrastive Consistency Search (CCS), rely on contrastive activation patterns and struggle with complex multi-step reasoning tasks, while mechanistic interpretability tools have primarily been used to \emph{understand} model behavior rather than to \emph{extract} hi

arXiv cs.CL·just now

From Context Shift to Stylistic Collapse: Why Training Objectives Matter More Than Scale

arXiv:2605.28826v1 Announce Type: new Abstract: In modern LLMs, linguistic features function not as stylistic artifacts but as probes of probability mass, allocated under training alignment objectives. Language models trained with contemporary pipelines exhibit severe reshaping of linguistic features, leading to extreme language re-distribution. While previous stylometric analyses explored linguistic differences between AI-generated and human texts, we focus on the reshaping plaguing the LLM training pipeline itself. We analyze 17 models (410M-100B+ parameters) across 24 linguistically-motivat

arXiv cs.CL·just now

RightNow-Arabic-0.5B-Turbo: An Open Sub-1B Arabic Language Model via Vocabulary Injection and Edge-First Deployment

arXiv:2605.28827v1 Announce Type: new Abstract: Open Arabic large language models split into two classes: sub-1B multilingual models that treat Arabic as an afterthought (Qwen2.5-0.5B, Falcon-H1-0.5B), and 7B-70B Arabic-specialized models that require a server to run (Jais, AceGPT, ALLaM, SILMA). The one published attempt at a sub-2B Arabic-specialized model, Kuwain-1.5B, never released its weights. We present RightNow-Arabic-0.5B-Turbo, a 518M-parameter Arabic-specialized decoder LLM built on Qwen2.5-0.5B. The pipeline adds 27,032 Arabic tokens via mean-subtoken initialization, continues pret

arXiv cs.CL·just now

Micro-Macro Retrieval: Reducing Long-Form Hallucination in Large Language Models

arXiv:2605.28828v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve impressive performance across many tasks but remain prone to hallucination, especially in long-form generation where redundant retrieved contexts and lengthy reasoning chains amplify factual errors. Recent studies highlight a critical phenomenon: the closer key information appears to the model outputs, the higher the factual accuracy. However, existing retrieval-augmented language models (RALMs) lack effective mechanisms to ensure this proximity - external evidence is injected into reasoning via multi-turn ret

arXiv cs.CL·just now

Aryabhata 2: Scaling Reinforcement Learning for Advanced STEM Reasoning

arXiv:2605.28829v1 Announce Type: new Abstract: Competitive STEM examinations such as JEE and NEET require multi-step symbolic reasoning, precise numerical computation, and deep conceptual understanding across physics, chemistry, and mathematics. Recent large language models perform strongly on common reasoning benchmarks, yet they remain difficult to deploy at scale, where millions of student doubts demand domain-specific, consistently structured problem solving. We introduce Aryabhata 2, a reasoning-focused language model for competitive STEM examinations, trained via reinforcement-learning

arXiv cs.CL·just now

Funding Hot

Benchmarking Open-Source Safety Guard Models: A Comprehensive Evaluation

arXiv:2605.28830v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in safety-critical applications, robust content moderation becomes essential. We present a comprehensive evaluation of 14 open-source safety guard models on a curated benchmark of 79,331 samples spanning 8 NIST AI Risk Framework safety categories. Our benchmark aggregates four diverse datasets (HarmBench, StrongREJECT, RealToxicityPrompts, and BeaverTails), filtered to focus exclusively on safety-relevant content (violence, hate speech, harassment, sexual content, suicide/self-harm, profan

arXiv cs.CL·just now

S3Mem: Structured Spatiotemporal Scene-Event Memory for Long-Horizon Interactive Question Answering

arXiv:2605.28831v1 Announce Type: new Abstract: Long-horizon interactive agents often accumulate large trajectory histories yet still fail to answer questions about earlier events reliably. We argue that the main bottleneck is not context length alone, but the trajectory-to-answer interface of long-term memory. When histories are stored as plain-text chunks and queried with standard retrieval-augmented generation (RAG), systems often retrieve locally relevant but chain-incomplete evidence, especially for spatial, temporal, repeated-event, and multi-hop state questions. We propose S3MEM, a stru

arXiv cs.CL·just now

A comparative study of transformer-based embeddings for topic coherence

arXiv:2605.28832v1 Announce Type: new Abstract: Topic modeling is a branch of Natural Language Processing (NLP) that aims to organize large collections of texts into coherent groups according to word co-occurrence patterns, with Latent Dirichlet Allocation (LDA) remaining one of the most widely used and interpretable probabilistic approaches. Recent advances in NLP, particularly transformer-based language models, offer improved document representations. It is also known that the size of the model (in terms of number of parameters) has a significant impact in the performance of the language mod

arXiv cs.CL·just now

Transcribing Children's Speech: ASR Performance and Obtaining Reliable Orthographic Transcriptions

arXiv:2605.28833v1 Announce Type: new Abstract: Automatic speech recognition (ASR) has the potential to substantially reduce manual annotation effort in child speech research by generating automatic transcriptions. However, obtaining reliably high-quality ASR transcriptions for child speech remains challenging in low-resource languages due to limited child-specific pre-trained models and highly diverse noise conditions. This study investigates the effectiveness of state-of-the-art ASR models on child speech through two research questions, by evaluating nine ASR models from three model families

arXiv cs.CL·just now

Tools Hot

datasette 1.0a31

Release: <a href="https://github.com/simonw/datasette/releases/tag/1.0a31">datasette 1.0a31</a> Another significant alpha release, with two new headline features. <blockquote> Datasette now offers users with the necessary permissions the ability to both execute write queries against their database and to save stored queries (renamed from "canned queries") both privately and for use by other members of their Datasette instance. </blockquote> There's more detail in <a href="https://datasette.io/blog/2026/sql-write-queries

Simon Willison·just now

Models Hot

Strengthening societal resilience with Rosalind Biodefense

OpenAI launches Rosalind Biodefense, expanding trusted access to GPT-Rosalind for vetted developers and U.S. government partners advancing biodefense, public health, and pandemic preparedness through frontier AI.

OpenAI Blog·just now

Tools Hot

Anthropic's run-rate revenue hits $47 billion

The most interesting thing about <a href="https://www.anthropic.com/news/series-h">Anthropic's $65B Series H announcement</a> is this line (emphasis mine): <blockquote> Since our Series G in February, adoption has continued to grow across global enterprise customers, and our run-rate revenue crossed $47 billion earlier this month. </blockquote> Anthropic have made a bit of a habit of sharing their "run-rate revenue" in this kind of announcement, which is an annualized projection of their current revenue - typically calculated by taking the most recent month an

Simon Willison·just now

Glean’s top line crosses $300M as AI budget cutting becomes its major selling point

The enterprise AI search startup tripled its annual revenue even as tech giants entered the category.

TechCrunch AI·just now

Tools Hot

Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

Hugging Face Blog·just now

Models Hot

Claude Opus 4.8: "a modest but tangible improvement"

Anthropic shipped <a href="https://www.anthropic.com/news/claude-opus-4-8">Claude Opus 4.8</a> today. My favourite thing about it is this note in the release announcement: <blockquote> Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor. There’s still more to be done: we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost. </blockquote> It's so refreshing to see an AI lab honestly describe a release as a minor incremental improvement over the previous model! Honesty seems to be

Simon Willison·just now

Models Hot

llm-anthropic 0.25.1

Release: <a href="https://github.com/simonw/llm-anthropic/releases/tag/0.25.1">llm-anthropic 0.25.1</a> <blockquote> <ul> <li>New model: <a href="https://www.anthropic.com/news/claude-opus-4-8">Claude Opus 4.8</a> (<code>claude-opus-4.8</code>).</li> <li>New <code>-o fast 1</code> option for <a href="https://platform.claude.com/docs/en/build-with-claude/fast-mode">fast mode</a>, for organizations with that feature enabled on their account.</li> <li>Default max_tokens for each model now defaults to that model's maximum output rather than 8,192. <a href="https://github.co

Simon Willison·just now

The internet is being rebuilt for machines

As AI agents move from experiments to production, AWS, Cloudflare, and others are redesigning cloud infrastructure for a future dominated by machine-generated internet traffic instead of human users.

TechCrunch AI·just now

Microsoft 365 Copilot gets a speed boost and cleaner design

Microsoft is launching a revamped version of Microsoft 365 Copilot, offering a cleaner design that the company claims loads twice as fast. As part of this update, Copilot will provide more reliable and structured responses that are easier to scan, according to Microsoft. The redesign, which is rolling out across desktop and mobile devices, comes […]

The Verge AI·just now

Asana acquires no-code agent-builder StackAI

Asana will incorporate StackAI into its growing suite of AI workflow tools.

TechCrunch AI·just now

Tools

markdown-svg-renderer

Tool: <a href="https://tools.simonwillison.net/markdown-svg-renderer">markdown-svg-renderer</a> A slightly customized Markdown rendering tool with special treatment for fenced code SVG blocks - it both renders the image and provides a tab for switching to the code view. You can paste in Markdown or give it a URL to a CORS-enabled Markdown file or Gist. <a href="https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2Ffea4f7546626d627862dc241a4e3a86a">Here's an example</a> where it loads a Markdown file full of LLM peli

Simon Willison·just now

Anthropic raises $65 billion, nears $1T valuation ahead of IPO

Anthropic has closed a $65 billion Series H round at a $965 billion post-money valuation, marking what could be the AI startup's final private fundraise before a highly anticipated IPO.

TechCrunch AI·just now

Policy

Just like gold and oil, we’ll soon be able to trade AI token futures

Large exchanges are designing derivative products around AI tokens, which are increasingly being considered less a computational output and more a raw material input, like electricity or bandwidth.

TechCrunch AI·just now

Tools

How a new extraction process could unlock the world’s lithium

Researchers say they’ve found a new way to extract lithium, a crucial metal used in the lithium-ion batteries that power electric vehicles and energy storage arrays. This new technique could be more environmentally friendly and cheaper than existing ones. The research was published today in Science, and a startup called Rock Zero is working to commercialize the process. “At scale, we believe this will be the lowest-cost way of sourcing lithium in the world,” says Yet-Ming Chiang, one of the study authors, who is an MIT professor and a serial entrepreneur behind climate tech companies includin

MIT Technology Review·just now

In just 3 weeks, StrictlyVC is coming to Los Angeles

StrictlyVC Los Angeles is on June 18. Join for meaningful networking and fireside chats with leaders from Mach Industries, Shinkei Systems, and more. Register today.

TechCrunch AI·just now

Models

Anthropic releases Opus 4.8 with new ‘dynamic workflow’ tool

The new Opus model comes with a tool called Dynamic Workflows, for coordinating swarms of subagents.

TechCrunch AI·just now

Models

Claude’s new model is more ‘honest’ when it messes up

Anthropic is releasing Claude Opus 4.8 on Thursday, and the company is touting the model's "honesty." According to Anthropic, it trains "all [its] models to be honest - for instance, to avoid making claims that they can't support." But it notes that "a general problem with AI models is that they sometimes jump to conclusions, […]

The Verge AI·just now

A $2,000 AI-generated film will make its debut at Tribeca

Next month's Tribeca Festival will include the premiere of an AI-generated film: Dreams of Violets. The 75-minute film is a fictional dramatization of the Iranian government's mass killing of protestors in January, with the people and images fully created by AI, as reported earlier by The Hollywood Reporter. Dreams of Violets cost $2,000 to make […]

The Verge AI·just now

YouTube takes baby steps to being a real podcast app

New features coming to YouTube could make it better for listening to podcasts, rolling out to Premium subscribers starting today on Android and coming later to iOS. A new "on-the-go mode" shifts YouTube into an audio-first layout, with larger, simplified playback buttons, a still image in place of the video, and a timeline showing video […]

The Verge AI·just now

How long is Anthropic’s lease with SpaceX? Opinions vary

Elon Musk is publicly reframing xAI’s massive Anthropic compute deal as short-term and cancellable, despite SpaceX’s own S-1 filing describing payments through May 2029.

TechCrunch AI·just now

Sesame, the conversational AI startup from Oculus founders, launches its iOS app

Sesame’s new iOS app brings its conversational AI agents to the public, offering more natural back-and-forth interactions designed to feel less like traditional chatbots and more like talking to a person.

TechCrunch AI·just now

These new iOS 27 renders hint at Siri’s big redesign

Apple's long-awaited Siri overhaul, expected to arrive in iOS 27, might look a lot like ChatGPT with a splash of Liquid Glass. Renders from Bloomberg offer a preview of iOS 27, including the new app and chat interface for Siri. The renders are "based on information viewed by Bloomberg and people with knowledge of [Apple's] […]

The Verge AI·just now

Policy

CNN sues Perplexity over ‘verbatim’ copycat articles

CNN has filed a lawsuit against Perplexity, claiming that the startup's AI tools generate "verbatim" copies of its work, as reported earlier by CNN. The lawsuit, filed in a New York court on Thursday, also alleges that Perplexity provides users with information locked behind CNN's subscription. Perplexity, which offers an AI "answer" engine along with […]

The Verge AI·just now

Rivian’s software chief thinks you don’t need CarPlay or buttons

Today, I’m talking with Wassym Bensaid, the chief software officer at Rivian, and the co-CEO of Rivian’s platform joint venture with Volkswagen, which everyone just calls RV Tech. That joint venture kicked off about a year and a half ago with a nearly $6 billion investment from Volkswagen. It effectively puts Wassym in charge of […]

The Verge AI·just now

The Download: climate tech goes public and the AI Hype Index returns

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. Climate tech companies are going public. What’s next? Solar and battery company Solv Energy went public in February, hitting a $6 billion valuation. X-energy, which builds small modular nuclear reactors, followed at $11.5 billion. Then came geothermal company Fervo Energy, reaching a market cap of about $12.4 billion. All three have been IPO success stories. And it doesn’t feel like a coincidence that they’re racing to provide electricity in an era of risin

MIT Technology Review·just now

Models

How Endava builds an agentic organization with Codex

Learn how Endava uses Codex to build an agentic organization, accelerating software delivery and reducing requirements analysis from weeks to hours.

OpenAI Blog·just now