Look Up Say More Top creators, ranked Gift Ideas For Everyone On Your List Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Creator Playbook Mashable Selects In My Bag AI at School Safety Net Versus Trending Now All Series Is Audible actually worth it in 2026? I keep cancelling, but it drags me back in. Christina Buff is a Nashville-based freelance writer for who covers shopping with a splash of entertainment. If you're ever wondering what streaming service you need to watch something (and the cheapest way to sign up for it), she's your girl. All products featured here are independently selected by our editors and writers.

AI at School Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Look Up Say More Safety Net Versus Creator Playbook In My Bag Trending Now Back to School Good Connection: Uplifting stories for a digital age All Series The 10 best free dating apps that won't charge you just to send a message Swipe, match, and chat for $0. Anna Iovine is the associate editor of features at Mashable. Previously, as the sex and relationships reporter, she covered topics ranging from dating apps to pelvic pain. Before Mashable, Anna was a social editor at VICE and freelanced for publications such as Slate and the Columbia Journalism Review. Follow her on Instagram . Tabitha Britt is an award-winning freelance journalist, editor, and SEO/AEO strategist. Aside from reviewing dating apps and sex toys for Mashable, Tabitha is also the founding editor-in-chief of DO YOU ENDO -- a digital magazine by individuals with endometriosis, for individuals with endometriosis. She has a Master's degree in Creative Publishing and Critical Journalism from The New School for Social Research and is a grad of Sextech School. You can find more of her work in various online publications, including,, and . Editors and writers independently select products unless marked Sponsored or Promoted.

A paid plan makes the most sense when you regularly run into the Free tier's limits or rely on tools reserved for higher tiers. If that's no longer true, paying every month gets harder to justify. One option is ChatGPT Go, a lower-cost paid tier that OpenAI now offers in every country where ChatGPT is supported. Spending less doesn't necessarily require giving up a paid plan altogether. AI subscriptions have another unusual wrinkle: the product can change while you are paying for it.

OpenAI is giving some members of its Daybreak cybersecurity program access to a new model that's less likely to refuse higher-risk tasks. The company is also expanding access to Daybreak to more partners, including Accenture, IBM, CrowdStrike, Cisco, Sophos and Cloudflare. OpenAI says the companies will use the cyber models available through Daybreak to protect their customers. Under the expanded program, Daybreak is available to partners in two tiers. Daybreak Blue gives them access to frontier general-purpose models, including GPT‑5.6 Sol, OpenAI's most advanced one yet.

Meta has announced an early beta of Muse Code, a new coding agent meant to compete with Anthropic's Claude Code and OpenAI's Codex. The new terminal-based coding tool is powered by Muse Spark 1.2, a new version of Meta's AI model which offers "improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows." Like its competition, Muse Code can handle software engineering tasks like writing code, planning changes and validating results, making it possible to build working software with text prompts. It can also manage multiple sub-agents and delegate tasks to complete work more efficiently. Meta's announcement includes sample projects like an interactive model of a photon sphere and a Plants vs. Zombies knockoff, and a demo video of the tool building a webpage based on an MP4 file.

Causal-discovery algorithms return a directed graph, yet provide no principled means of distinguishing edge directions identified by the data from those assigned without an identifying assumption. Under the standard Markov and faithfulness conditions, the observational distribution identifies only a Markov equivalence class; orientations within that class are not determined by the joint distribution and cannot be recovered from additional samples alone, but require either a functional restriction or an intervention. We introduce a protocol for observational causal discovery on continuous data that attaches to each candidate edge a discrete impossibility certificate: a RESOLVED code records the identifiability theorem under which the direction was committed, while an IMPOSSIBLE code records the failure mode together with the specific question a domain expert must answer to resolve it. The bivariate cascade is extended with five gated identifiability tiers LSNM, IGCI, Stein, MDL, and PEIT that abstain when their precondition test rejects. Two oracle primitives, the meta-hub query and the node-children query, jointly establish an upper bound of $1+K$ expert interactions sufficient to recover any DAG, where $K$ denotes the number of non-leaf vertices. Under an ideal-oracle assumption, the bound is met exactly on the asia, sachs, child, and alarm benchmarks.

Capped-usage SaaS products -- LLM subscriptions such as Claude Code and ChatGPT, cloud platforms such as Vercel and Cloudflare Workers, corporate benefit platforms, identity-verification services with liability transfer -- share a structural signature with insurance products: a fixed premium decoupled from realized consumption, stochastic per-user demand with heavy-tailed severity, a non-fungible cap that resets on a fixed schedule, and a portfolio-level exposure that requires reserve adequacy under tail risk. We argue that this is not an analogy. It is the same operational problem actuarial science has been tooled for decades to address, restated with new dependent variables (tokens, bandwidth bytes, function-invocations, gym check-ins) in place of medical claims. This paper proposes a modeling framework for capped-usage SaaS pricing built from frequency-severity decomposition, premium calculation principles, and Monte Carlo reserve adequacy. We map the framework to publicly observable subscription tiers in two domains (LLM services and cloud platforms), ground it in canonical health-insurance economics (Arrow 1963; Pauly 1968; Manning et al. 1987; Brot-Goldberg et al. 2017), and demonstrate divergence from traditional unit economics through a worked example. The contribution is operational rather than theoretical: not a new theorem, but vocabulary and tools currently absent from cs.LG/stat.ML practice.

Training loss and throughput can hide distinct internal representation in language-model training. To examine these hidden mechanics, we use spectral measurements as practical and operational diagnostics. Using a controlled family of decoder-only models adapted from the modded NanoGPT codebase, we introduce an empirical protocol based on activation covariance and per-sample gradient SVD spectra. This dual-view reveals three empirical findings and one mechanistic explanation. First, batch size acts as a latent determinant of representation geometry: runs that reach equal loss settle into systematically distinct activation spectra. Second, the activation covariance tail measured early in training reliably forecasts downstream token efficiency. Third, movement of the activation spectrum head (leading modes), together with gradient spectra, characterizes underlying learning-dynamics changes, separating learning-side architectural improvements from primarily execution-side gains. These predictive and diagnostic signals persist across the 12-, 36-, and 48-layer model tiers. Finally, a mechanistic model proves the main observations and explains how activation covariance spectra correlate with task-aligned feature learning.

How to claim Verizon's $20 outage credit Free and Go tier users in the US will start seeing sponsored content soon. A screenshot illustrating what ads will look like in ChatGPT. OpenAI plans to start testing ads inside of ChatGPT in the coming weeks. In a blog post published Friday, the company said adult users in the US of its free and Go tiers (more on the latter in a moment) would start seeing sponsored products and services appear below their conversations with its chatbot. Ads will be clearly labeled and separated from the organic answer, OpenAI said, adding any sponsored spots would not influence the answers ChatGPT generates.

Ads Are Coming to ChatGPT. OpenAI says ads will not influence ChatGPT's responses, and that it won't sell user data to advertisers. OpenAI plans to start testing ads inside ChatGPT in the coming weeks, marking a significant shift for one of the world's most widely used AI products. The company announced Friday that initial ad tests will roll out in the United States before expanding globally. OpenAI says ads will not influence ChatGPT's responses, and that all ads will appear in separate, clearly labeled boxes directly below the chatbot's answer.