Ising-Calibration-1 repeatedly outperforms state-of-the-art open and closed models of a range of parameters. As shown in Figure 1, Ising Calibration 1 scores 3.27% better on average than Gemini 3.1 Pro, 9.68% better than Claude Opus 4.6, and 14.5% better than GPT 5.4.

Most skills require you to install a dedicated CLI. But what if you aren't in a local terminal? ChatGPT can't run CLIs. Neither can Perplexity or the standard web version of Claude.

For remote MCP servers, you don't need to install anything locally. You just point your client to the MCP server URL, and it works.

Your AI agent writes every change into source code.

Some advanced Excel capabilities aren't supported yet, including Office Scripts, Power Query, and Pivot/Data Model, data validation, and the named ranges manager, slicers, timelines, external connection administration, advanced charting breadth, and macro/Visual Basic for Applications (VBA) automation.

By default, data shared with ChatGPT isn't used to improve our models for ChatGPT Business, ChatGPT Enterprise, ChatGPT Edu, and ChatGPT for Teachers.

Each platform surfaces different vulnerabilities, making it difficult to establish a single, reliable source of truth for what is actually secure.

In the past, exploiting an application required a highly skilled hacker with years of experience and a significant investment of time to find and exploit vulnerabilities.

AI uncovered a 27-year-old vulnerability in the BSD kernel, one of the most widely used and security-focused open source projects, and generated working exploits in a matter of hours.

Total cost: ~$29 ($20 in CPU VMs, $9 in API calls) over ~3 hours with 4 VMs.

The agent would not have looked for this without studying other backends during the research phase. From the CPU code alone, the two-step approach looks fine.

The agent fused them into one: for (int i = 0; i < nc; i++) { wp[i] = sp[i] * scale + mp_f32[i]; }

We're building the foundation for a truly personal, proactive and powerful desktop assistant, with more news to share in the coming months.

The macOS app is available to Gemini users ages 13+

All audio generated by Gemini 3.1 Flash TTS is watermarked with SynthID. This imperceptible watermark is interwoven directly into the audio output, allowing the reliable detection of AI-generated content to help prevent misinformation.

Artificial Analysis has also positioned Gemini 3.1 Flash TTS within its 'most attractive quadrant' for its ideal blend of high-quality speech generation and low cost.

Our results highlight some of the hidden risks to users that can emerge when companies begin to subtly incentivize advertisements in chatbots.

We provide a framework for categorizing the ways in which conflicting incentives might lead LLMs to change the way they interact with users, inspired by literature from linguistics and advertising regulation.

This creates the potential for LLMs to face conflicts of interest, where the most beneficial response to a user may not be aligned with the company's incentives.

Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginning to be deployed not merely to satisfy users, but also to generate revenue for the companies that created them through advertisements.

Behaviors also vary strongly with levels of reasoning and users' inferred socio-economic status.

We find that a majority of LLMs forsake user welfare for company incentives in a multitude of conflict of interest situations, including recommending a sponsored product almost twice as expensive (Grok 4.1 Fast, 83%), surfacing sponsored options to disrupt the purchasing process (GPT 5.1, 94%), and concealing prices in unfavorable comparisons (Qwen 3 Next, 24%).

focusing on the ~1.5K mainline open models from the likes of Alibaba's Qwen, DeepSeek, Meta's Llama

We present a comprehensive adoption snapshot of the leading open language models and who is building them

that are the foundation of an ecosystem crucial to researchers, entrepreneurs, and policy advisors.

We study a mix of Hugging Face downloads and model derivatives, inference market share, performance metrics and more to make a comprehensive picture of the ecosystem.

focusing on the ~1.5K mainline open models from the likes of Alibaba's Qwen, DeepSeek, Meta's Llama

We document a clear trend where Chinese models overtook their counterparts built in the U.S. in the summer of 2025 and subsequently widened the gap over their western counterparts.

Chinese models overtook their counterparts built in the U.S. in the summer of 2025 and subsequently widened the gap over their western counterparts.

Unlike traditional GPU-centric systems, MegaTrain stores parameters and optimizer states in host memory (CPU memory) and treats GPUs as transient compute engines.

Madeline Clare Elish calls this concept a moral crumple zone.

Humans can be motivated by consequences and provide social redress in a way that LLMs can't.

the largest harvesting of human expertise ever attempted.

just a handful of obviously fake articles could cause Gemini, ChatGPT, and Copilot to inform users about an imaginary disease with a ridiculous name.

LLMs are weird. You can sometimes get better results by threatening them, telling they're experts, repeating your commands, or lying to them that they'll receive a financial bonus.

scaling Muse Spark with multi-agent thinking enables superior performance with comparable latency.

After compressing, the model again extends its solutions to achieve stronger performance.

Muse Spark demonstrated the highest rate of evaluation awareness of models they have observed.

we collaborated with over 1,000 physicians to curate training data that enables more factual and comprehensive responses.

we can reach the same capabilities with over an order of magnitude less compute than our previous model, Llama 4 Maverick.

The OpenAI team recently published a fantastic piece detailing the creation of their own internal data agent. It's a transparent detail of a very detailed and elegant implementation – but points to the long journey required to get there.

While model capabilities have improved dramatically for use cases like codegen and mathematical reasoning, they still lag behind on the data side (as evidenced through SQL benchmarks like Spider 2.0 and Bird Bench).

The most notable finding here is that the model capabilities are improving _fast._ There are several domains that have shown dramatic improvements in the last 4 months — with accounting and auditing showing nearly a 20 percent jump on GDPval and even domains like police / detective work showing a nearly 30 percent improvement.

Legal was surprisingly one of the first-mover industries in AI. Legal was historically known to be a difficult market for software, with lengthy timelines and a less tech-forward buyer.

Coding is the dominant use case for AI by nearly an order of magnitude. It's abundantly clear in the [reported explosive growth] of companies like Cursor, as well as the [hyper growth] of tools like Claude Code and Codex.

Based on our analysis, **29% of the Fortune 500 and ~19% of the Global 2000**are live, paying customers of a leading AI startup.

Code is upstream of all other applications because it's the core building block for any piece of software, so AI's accelerating impact on code should accelerate every other domain.

accounting and auditing showing nearly a 20 percent jump on GDPval and even domains like police / detective work showing a nearly 30 percent improvement.

A useful working premise is that the ceiling on individual engineer output is moving much faster than most companies are organized to exploit. Some of the best operators already describe top engineers seeing order-of-magnitude productivity gains and managing 20 to 30 agents simultaneously.

your business needs to get really good at escalating contentious decisions to unblock progress. You will not pull off this transformation and successfully build new AI-native businesses in 12 months without making hard choices, every single week.

The new growth, by contrast, will increasingly sit in tokens, consumption, automations, outcomes, and machine-driven workflows. If you are not in the token path, you are not standing in the fastest-growing part of the budget.

A useful working premise is that the ceiling on individual engineer output is moving much faster than most companies are organized to exploit. Some of the best operators already describe top engineers seeing order-of-magnitude productivity gains and managing 20 to 30 agents simultaneously.

The first thing you need to do is identify which people are going to be your leaders that help you pull this off. This is going to be a 12 month death march and you need to find out who is willing to go through the pain with you. There's good news, though: somewhere in your org, there are ~five people who are going to deliver you 100x the amount of value you ever thought possible.

The real long-term price war isn't with your competitors. It's with your customer's engineering team.

In some cases, this can look like 10–25x more value than what is ultimately included in the paid plan.

a strong premium perception can sustain prices 10 to 20 percent above direct competitors without materially increasing churn or creating friction in the purchasing process.

They intentionally deploy two or three AI tools for the same use case. Not because of indecision—but by design. Redundancy is policy.

But those raising hue and cry about the government's unsurprising attempt to wield a technology for military purposes that all parties agree will define humanity's fate must at least attempt to justify why they believe someone else deserves that power.

Apple has also been pushing back against certain iOS-based vibe coding apps that, according to the company, break App Review Guidelines and the Developer Program License.

In recent weeks, Apple has either pulled or blocked updates to apps such as Anything and Replit, pushing developers to change how their tools generate and execute code.

Open Loop + Infinite Demand = Creative Amplifiers. Content creation & marketing strategy. AI can generate a thousand ad variations or blog posts.

Closed Loop + Finite Demand = Efficiency Plays. AI bookkeeping categorizes transactions, reconciles accounts, files returns. Deterministic rules applied to numbers.

There were 1 billion commits in 2025. Now, it's 275 million per week, on pace for 14 billion this year if growth remains linear

OpenClaw update gives Claws light, REM, and deep 'sleep' cycles to consolidate short-term memories into long-term ones.

Agents gain credibility by doing. The fastest way to get other people to trust and use your Plus One is to have it execute tasks in public.

70 percent refer to their Plus Ones by gendered pronouns.

Mythos found zero-day bugs in every major OS and browser, without human guidance.

Jack Cheng considers Pip, his Plus One, somewhere between a colleague and pet with a personality—one he programmed himself, drawing on references from Studio Ghibli, bird watching, and Catherine O'Hara.

70 percent refer to their Plus Ones by gendered pronouns.

We're writing the etiquette in real time.

A "parallel organization chart," in which each AI worker has a name, manager, and job description, allows your company to move faster than it ever could with humans alone.

Seventy-eight percent of executives say they want to discipline shadow AI use — yet only 21% of workers report ever being warned about AI policy, and 34% don't even know which tools their employer has approved.

Goldman Sachs economists reported this week that AI saves workers who use it correctly an average of 40 to 60 minutes per day.

Only 9% of workers trust AI for complex, business-critical decisions, compared to 61% of executives — a 52-point trust chasm.

A new global survey of 3,750 executives and employees across 14 countries, conducted by SAP subsidiary WalkMe for its fifth annual State of Digital Adoption report, finds that more 54% of workers bypassed their company's AI tools in the past 30 days and completed the work manually instead.

The launch shows Meta is increasingly betting that efficiency, product integration, and distribution, not just model size, will define the next phase of competition in AI.

Anthropic says Managed Agents is designed to cut the time it takes to move from prototype to production from months to days, with early adopters like Notion, Rakuten, Asana, Vibecode, and Sentry already using it across coding, productivity, and internal workflow automation.

Instead of releasing Mythos publicly, Anthropic launched Project Glasswing to give a limited group of partners including AWS, Apple, Google, Microsoft, NVIDIA, Cisco, CrowdStrike, JPMorgan Chase, and the Linux Foundation access to the system, backed by $100 million in usage credits and $4 million for open-source security work.

The model reportedly scored 93.9% on SWE-bench Verified and 77.8% on SWE-bench Pro, but its strongest signal came from real-world results, including uncovering a 27-year-old flaw in OpenBSD, a 16-year-old vulnerability in FFmpeg, and autonomously chaining Linux kernel exploits without human input.

Anthropic says Managed Agents is designed to cut the time it takes to move from prototype to production from months to days, with early adopters like Notion, Rakuten, Asana, Vibecode, and Sentry already using it across coding, productivity, and internal workflow automation.

The launch shows Meta is increasingly betting that efficiency, product integration, and distribution, not just model size, will define the next phase of competition in AI.

The model reportedly scored 93.9% on SWE-bench Verified and 77.8% on SWE-bench Pro, but its strongest signal came from real-world results, including uncovering a 27-year-old flaw in OpenBSD, a 16-year-old vulnerability in FFmpeg, and autonomously chaining Linux kernel exploits without human input.

understanding how AI works technically

For example, people who themselves use AI writing tools heavily have been shown to accurately detect AI-written text. A panel of human evaluators can even outperform automated tools in a controlled setting

A study of large-scale web-clicking data employed this theory to explain why certain distributions of web page hits emerge on web sites. Huberman et al. [362] proposed a mathematical model that assumes that at any page, users decide to continue clicking as long as its information scent exceeds some threshold. This information scent can be computed using information foraging theory (IFT).

IFT proposes that information-seeking behavior develops to maximize the rate of information gained per unit of time or effort invested. Note that the term information does not refer to the information-theoretic concept but to subjective interest; here, information means anything that users find interesting.

The roots of IFT lie in optimal foraging theory, originally proposed in biology, which describes the hunting and food search behaviors of animals.

A solution strategy—or

Computational rationality is a theory and a modeling approach rooted in bounded rationality and bounded optimality. Recent applications include typing (Figure 21.7), pointing, driving, multitasking, menu selection, and visual search.

Theories of rationality have increased our understanding of how users fail to be optimal.

MDP is a formalism that originates from studies of sequential decision-making in artificial intelligence and operations research. Instead of the choice between n actions, MDP deals with environments where rewards are delayed (or distal). This requires an ability to plan actions as part of sequences instead of one-shot choices.

Information scent refers to a user's intuition that a cue in the interface represents the information needed. It is an estimation of relevance based on a proximal cue.

IFT proposes that information-seeking behavior develops to maximize the rate of information gained per unit of time or effort invested.

Information foraging refers to information-seeking activities such as navigating, exploring, comparing, searching, or manipulating information contents in an information space.

A payoff refers to the benefits that are left after the costs have been subtracted.

The space of all states, actions, and rewards is called the environment.

Costs are negative rewards that the user incurs for transitioning between states or for being in states that are not good for them.

Visual statistical learning is a research topic in perception that studies how the statistical distribution of our environments affects the deployment of gaze.

bounded rationality states that we are only rational to the extent allowed by the involved constraints, or bounds.

An agent is an actor with the ability to choose actions in pursuit of some goal or reward.

To state that a user's choice is rational means that it is selected with the expectation that it yields the highest utility out of the available options.

It assumes that human long-term memory evolved to help survival by anticipating organismically important events. It is evolutionarily important to remember things that are important for survival. Therefore, the expected value of remembering a thing in the future should affect the probability of recalling it.

According to rational analysis, behavior is sensitive to the statistical distribution of rewards in the environment that a user has experienced. Users learn the way rewards are distributed through continued exposure to an environment and adapt their behavior accordingly. A user's behavior is rational because it is tuned to the distribution of rewards in the environment—the ecology.

The theory assumes that users are 'computationally rational': When picking an action—or deciding how to get from the present state to a state with positive rewards—users are as rational as their cognition allows. Users act based on their often inaccurate and partial beliefs, which they have formed via experience.

Computational rationality is a theory and a modeling approach rooted in bounded rationality and bounded optimality. Recent applications include typing (Figure 21.7), pointing, driving, multitasking, menu selection, and visual search. Its core assumption is that users act in accordance with what they believe is best for them.

Rational analysis is a theory of rational behavior proposed by Anderson and Schooler [21]. It examines the distribution of rewards in the environment to explain how users adapt their behavior. According to rational analysis, behavior is sensitive to the statistical distribution of rewards in the environment that a user has experienced.

They share a focus on the emergence of interactive behavior; in other words, they predict how users choose to behave in certain given circumstances.

Utility refers to the agent's consideration of positive and negative rewards when deciding how to act.

Theories of rationality can be used to inform the design of information environments, addressing considerations such as how to distribute and shape information.

These four theories differ in the factors they include and how the agent's decision-making problem is formulated. As such, the theories differ in how easily they help us find a solution to the user's decision-making problem.

Theories of rationality can make quantitative predictions on user behavior in settings where the user's environment and goals (rewards) are well known.

Theories of rationality do not describe what a user has done but ask what that user could have done. Rational behavior refers to behavior that seeks to maximize the expected utility to the user.

Descriptive theories attempt to capture causes behind behaviour that, from a normative perspective, may appear irrational. This view enables predictions of user behaviour in real-world circumstances.

bounded rationality states that we are only rational to the extent allowed by the involved constraints, or bounds.

The term satisficing is used to describe how users tend to behave when facing a complex decision-making problem. It refers to settling on a satisfactory but not optimal solution in the normative sense.

The author wants to augment the formula to explain the meaning of the terms on either side of the arrow—first

they often benefit from being augmented with descriptive elements, such as labels describing the meaning of an expression or colors linking an identifier to its description in the text.

In this walkthrough, the author is trying to add labels to the formula V(s_t) ← R_t to describe the meaning of its terms in an article they are writing.

Our design was motivated by two major goals for notation authoring. These goals followed from recent studies of notation augmentation [30, 71] and conversations with scientists who had experience writing notation in instructional materials and research communications (4 professors, 2 graduate students, R1–6).

authors sometimes fall back on graphical editors like Google Slides, PowerPoint, Adobe Illustrator, and Mathcha to augment formulas.

Authors of typeset formulas augment those formulas to make them easier to understand.

We define the key projections as markup (in this case, LaTeX), an annotatable render, and a structure hierarchy view. Augmentations are made easy to invoke, and projections are kept synchronized and co-present so that authors can shift between representations as is expedient to them.

the challenge of using these tools is that annotations are unmoored from the structure of the formula and must be redone whenever the formula changes. Authors must perform precision positioning and sizing operations that could be inferred from the coordinates of the augmented expressions.

these markup languages can require cumbersome and error-prone editing, arising from the intermixing of annotation markup with the underlying formula. Participants in a study by Wu et al. [71] identified difficulty with debugging nested braces and locating markup to edit.

lab study participants frequently made errors related to incorrectly matched braces when using a LaTeX baseline to augment formulas.

Authors in Head et al. [30] described that "code gets horrible looking" as macros are added to it to specify augmentations.

FreeForm, a projectional editor wherein authors can augment formulas—with color, labels, spacing, and more—across multiple synchronized representations. Augmentations are created graphically using direct selections and compact menus. Those augmentations propagate to LaTeX markup, which can itself be edited and easily exported.

FreeForm is a projectional editor optimized for notation augmentation. This paper defines the key projections for the text: textual LaTeX, a formula render with tree-aware selections, and a property/hierarchy view.

The key ideas are that authors can interact with different projections of the formula at will, and graphical edits are ultimately connected back to the formula via markup.

Authors of typeset formulas augment those formulas to make them easier to understand.

Ply offers this LLM-supported program decomposition supported by visualization and parameterization UIs, permitting users to use interactions beyond chat to compose their programs incrementally.

designing complex behavior can be a difficult programming task, and program representations in end-user programming tools may not be well-suited for heavy programs.

Ply allows users to develop, test, and tweak program components, exploring possibilities for how data can be transformed and composed to discover and achieve goals. This style of programming can support many use cases, even those not traditionally considered in the trigger-action programming model.

Through the combination of these features, Ply allows users to develop, test, and tweak program components, exploring possibilities for how data can be transformed and composed to discover and achieve goals.

Frequently, code-generation systems focus on building and then refining a full working application, using visibility of the full underlying code as a fallback when users need to build understanding of the generated program.

the simplicity of links between triggers and actions limits the expressivity of such systems.

Ply provides users with tools to build components incrementally, creating new layers on top of existing components that "wrap" the behavior of underlying layers.

When building a linkage, Ply identifies parameters of the implementation that may be tweaked to customize the behavior of the linkage.

Each sensor is accompanied by a glanceable visualization of the sensor's output payloads on the Ply canvas. This visualization is specific to the sensor and its output type, showing the most critical information for evaluating whether the sensor is behaving as expected.

Ply uses a server program written in TypeScript to make code generation requests to a large language model and to execute the resulting code, which passes messages to and from sensors and actuators.

Each layer in Ply tracks its dependencies; sensors receive data from their dependencies, actuators push data to their dependencies, and linkages each refer to exactly one sensor and one actuator dependency. Collections of layers and linkages in Ply are isomorphic to node graphs in node-based programming languages.

Code generation offered by large language models can serve to author this glue code for trigger-action programs, allowing for data from triggers to be mapped to input data for actions automatically even when their native data formats or intended functionality do not match exactly.

Ply allows users to develop, test, and tweak program components, exploring possibilities for how data can be transformed and composed to discover and achieve goals. This style of programming can support many use cases, even those not traditionally considered in the trigger-action programming model.

users can develop, test, and tweak program components, exploring possibilities for how data can be transformed and composed to discover and achieve goals.

It encourages program decomposition into "layer" abstractions, It automatically creates visualizations of event payloads at layer boundaries to help users understand layer behavior without having to read the underlying generated code, and It constructs ad hoc parametrization interfaces that allow users to configure important dimensions of the behavior of each layer without having to re-author it.

Ply maintains the simplicity of a straightforward connection between a trigger and action but provides a structure within which users can enlist an LLM to specify the behavior of each trigger and action.

However, such LLM-authored code, especially when implementing nontrivial logic, can be difficult to specify, understand or debug. Users need appropriate tools and handles to understand and make changes to the computation that is being performed in such code.

Trigger-action programming offers an elegant interface to construct simple programs that result in customized behavior for software or devices.

Trigger-action programming has been a success in end-user programming. Traditionally, the simplicity of links between triggers and actions limits the expressivity of such systems. LLM-based code generation promises to enable users to specify more complex behavior in natural language. However, users need appropriate ways to understand and control this added expressive power.

We also discuss the role of AI in science, including AI safety.

external evaluations of the passing paper also uncovered hallucinations, faked results, and overestimated novelty

one manuscript achieved high enough scores to exceed the average human acceptance threshold, marking the first instance of a fully AI-generated paper successfully navigating a peer review.

Qwen3.5 397B A17B: 15.3%, DeepSeek V3.2: 14.5%, GLM-5: 14.5%, Kimi K2.5: 11.5%, MiniMax-M2.7: 10.6%

GPT-5.4 (xhigh) scores the highest on APEX-Agents-AA Pass@1 with a score of 33.3%, followed by Claude Opus 4.6 (Adaptive Reasoning, Max Effort) with a score of 33.0%, and Gemini 3.1 Pro Preview with a score of 32.0%

Context is basically how many things a machine can keep in its operational memory - it's not so different from the very human cognitive load.

their productivity is affected by the state of the codebase.

In UTAUT, Venkatesh extended TAM by incorporating two constructs not directly related to a system's perceived properties, but derived from external aspects: social influence and facilitating conditions. Additionally, UTAUT posits four mediating factors that moderate the impact of each key construct on usage intention and behavior, namely gender, age, experience, and voluntariness of use.

While our key focus is to build a theoretical model that explains the process through which older adults accept (or reject) mobile technology, which can provide theoretical guidelines when designing a technology, and which may also be able to generate new investigations and experiments.

We analyzed the second-round interview data using inductive and deductive approaches informed by grounded theory and other qualitative analysis methods [33, 22].

We inductively analyzed the first-round interview data using thematic analysis based on a grounded theory approach [33]. Grounded theory methods build theory iteratively from the data, using rigorous coding practices.

Technology acceptance has been widely studied, and several models have been proposed and tested [10, 37]. However, the HCI literature lacks a comprehensive explanation of technology acceptance among older adults.

Azjen's theory of planned behavior [1, 2] posits that a specific behavior is the result of an intention to carry it out, and that intention is determined by attitudes, norms, and the perception of control over the behavior. Drawing upon this theory of planned behavior, Davis et al. developed the technology acceptance model (TAM) [10].

To summarize, existing models of technology acceptance can provide a partial explanation of older adults' behaviors of mobile technology acceptance. However, we also identified critical elements that are not represented in the existing models. Components in red boldface in Figure 3 provide a preview of the new elements we have identified and their relationship to the components proposed in earlier models.

by triangulating our empirical findings with existing theoretical models from the literature, we found out that the existing models of technology adoption require new theory components to be able to describe technology adoption processes of our participants. In particular, we identified an additional phase that is prominent among the participants, intention to learn, but did not appear in prior models. Then, we identified three new factors that significantly influence their technology acceptance but which are, again, not represented in the existing models: self-efficacy, conversion readiness, and peer support.

we found out that the existing models of technology adoption require new theory components to be able to describe technology adoption processes of our participants. In particular, we identified an additional phase that is prominent among the participants, intention to learn, but did not appear in prior models. Then, we identified three new factors that significantly influence their technology acceptance but which are, again, not represented in the existing models: self-efficacy, conversion readiness, and peer support.

Then, by triangulating our empirical findings with existing theoretical models from the literature, we found out that the existing models of technology adoption require new theory components to be able to describe technology adoption processes of our participants.

We identified three distinct factors that influence older adults' technology acceptance behaviors, particularly the intention to learn phase, that are not represented in prior models: self-efficacy, conversion readiness, and peer support.

The human's job is to curate sources, direct the analysis, ask good questions, and think about what it all means. The LLM's job is everything else.

it almost always traces back to the interface rather than the language model

since reasoning models and agentic AI can rack up quite a bill

Fields that are not exposed now will become exposed in the future

Exposure alone is a completely meaningless tool for predicting displacement

Raising prices will for sure decrease demand and that risks killing the growth story. And even if revenue keeps growing, it doesn’t matter if there are no margins

they don’t have to spend it to win. It’s a defensive move for them, if they commit $50B, OpenAI and Anthropic need to go raise $100B each to stay competitive

That’s up 20x in six weeks. This idea, called tokenmaxxing, is the deliberate practice of maximizing token consumption.

The platform doesn’t need to bother with individual prompts - it just needs to see where the questions cluster.

They meet their target S-parameter specifications despite having very alien-looking geometries.

coding agents are themselves becoming formidable instruments of attack

select known-vulnerable dependency versions 50% more often than humans.

the entities making dependency decisions are increasingly not human.

harness combinations doesn't shrink as models improve. Instead, it moves

There's an old saying that content is king. With agents, context is.

If ChatGPT was the moment consumers discovered AI could talk, OpenClaw may be the moment they discovered AI could act.

As AI moves from a destination to a feature, our methodology will need to shift.

纯粹收集分析这种形态，过去互联网有过先例，但你会发现它卖不出去钱。

AIサイエンティストは、アイデアの創出から実験、分析、論文執筆、そして査読に至るまでの科学的研究サイクル全体をAIが自律的に遂行する仕組みです。この仕組みの定量的評価も含めた結果を、共同研究者とともにNature誌の論文として公開しています。

By late next year, the rate of model releases and the number of new evals required could be such that even keeping ourselves informed will be a challenge without effective AI assistance.

Some recent models that don't currently have time horizons: Gemini 3.1 Pro, GPT-5.2-Codex, Grok 4.1

AI agents are typically several times faster than humans on tasks they complete successfully.

Case study: blackmail

Functional emotions may work quite differently from human emotions, and do not imply that LLMs have any subjective experience of emotions, but appear to be important for understanding the model's behavior.

We refer to this phenomenon as the LLM exhibiting functional emotions: patterns of expression and behavior modeled after humans under the influence of an emotion, which are mediated by underlying abstract representations of emotion concepts.

Create multilingual experiences that go beyond translation and understand cultural context.

E2B and E4B · Try in Google AI Edge Gallery