Participants felt that when the work reflected their "signature style" (P4, nonfiction writer) or "distinctive mark" (P8, web developer), they had a stronger sense of creative ownership.

Participants used a variety of words to get this message across: self-indulgence, passion, obsession, vulnerability. Being able to engage in their own explorations, share their backgrounds and experiences, and, in the words of one participant, "imbue more of [themselves]" (P9, dancer), was key across the study.

P19 (painter, glass artist) chose a piece that was an exploration of body and memory: "It was a lot of looking through and reflecting what I was thinking."

P4 (nonfiction writer) cited that they chose the work because it was both crafted in their signature style, and was an emotional piece written about their mother.

Embodiment of values, personality, and identity was repeatedly cited by participants as a strong reason why they feel creative ownership over their work.

Embodiment – How much do you feel that the finished product embodies your values, personality, and identity?

Qualitatively, pre-framework talk concentrated on a limited subset of subdimensions (embodiment, control, abstraction). Once introduced, participants articulated and prioritized all nine subdimensions, enabling finer distinctions (e.g., conceptual authorship vs. physical production) and revealing medium-dependent nuances.

Participants also found the categories legible, and a recurrent split emerged between person-focused and process-focused practices. Employment context further moderated ownership: low-ownership projects were often job-driven, whereas high-ownership projects skewed toward self-initiated work. These findings support modeling ownership as a multi-dimensional profile with moderators rather than a single latent factor.

Pre-framework interviews concentrated on Embodiment, Control, and Abstraction. With the framework in view, attention distributed across all nine dimensions. Quantitatively, high-ownership cases exhibited higher overall scores, whereas low-ownership cases showed greater dispersion. Taken together, these patterns indicate that the framework broadens the analytic space of ownership and supports the capture of heterogeneous routes to ownership, particularly in low-ownership contexts.

Overall, these results demonstrate both the coverage and diagnostic power of the framework: all nine sub-dimensions shifted between conditions, and the variance patterns in the low ownership condition surfaced the diverse ways participants experience reduced ownership.

For HCI, the immediate use is practical: report ownership as a profile rather than a single score, state construct boundaries, and use the dimensions as design levers (e.g., decision rights for Control, intent alignment for Intentionality, attribution for Recognition, modality-aware workflows for Production/Abstraction, and role clarity for Interdependence).

Responses for low-ownership projects showed substantially greater variance, with wider inter-quartile ranges and more outliers than in the high-ownership condition. Whereas ratings for high-ownership projects clustered tightly at the upper end of the scale, low-ownership responses spanned nearly the full range, from near zero to moderately high values. This indicates that while participants converge on what constitutes high ownership, experiences of low ownership are more heterogeneous, reflecting different ways ownership may be diminished (e.g., limited control, lack of recognition, or minimal effort).

Methodologically, we recommend reporting an ownership profile rather than a single score and explicitly stating construct boundaries. A brief "ownership design card" in Methods—specifying manipulated versus measured dimensions, expected moderators (e.g., medium tangibility, employment context), and anticipated trade-offs—would improve interpretability and comparability.

Across all nine sub-dimensions of the framework—Embodiment, Occupancy, Recognition, Control, Intentionality, Effort, Production, Abstraction, and Interdependence—participants gave consistently higher ratings for projects they associated with high ownership compared to low ownership (Figure 2). This pattern held across the board, suggesting that the framework reliably distinguishes between ownership conditions rather than capturing isolated dimensions.

A potential risk is profile drift under sustained high-automation use (e.g., declines in perceived Effort or Control). Because the framework is lightweight, it can function as a periodic check-in to track such changes and recommend countermeasures (e.g., adding decision checkpoints or narrowing automation scope).

The framework yields actionable implications for system design. Treating ownership as a first-class experience goal positions each dimension as a design lever. Control can be protected by making decision rights explicit, keeping suggestions reversible, and attaching rationales to consequential edits. Intentionality can be supported through periodic intent check-ins and visual diffs that surface drift from initial goals. Recognition benefits from attribution by default. Production and Abstraction suggest modality-aware workflows (concept-first versus material-first), and Interdependence calls for role visibility and decision traceability in collaborative tools. The aim is not to prescribe features but to make ownership designable: systems can be tuned to the ownership profile a context demands.

In study of AI-driven scriptwriting by Weber et al. [42], participants associated ownership with ease, expression, collaboration, uniqueness, and enjoyment.

Weber et al. [43], for example, use the term "artistic ownership" in studying support for creative goals, yet operationalize it through adjacent concepts such as creative vision, intentions, collaboration, pride, control, and emotional response [43]. Even when researchers begin with a focused definition, as in Wasi et al.'s work [41] on content ownership, related ideas often surface—embodiment, identity, originality, and effort among them.

Some studies conflate ownership with adjacent ideas (e.g., control, vision, identity); others elicit participants' views without a common scaffold, making results hard to compare across settings and media.

As one participant put simply, "Did I love it?" (P3, dancer).

P4 (nonfiction writer) reported a similar sentiment but used the term pride instead — "That sense of proudness doesn't really have anything to do with how much I feel ownership about it, at least not directly."

P2 (ukulelist, singer) reported feeling a "creative attachment" to a piece, even though they didn't feel any ownership over it — "A little bit of my heart and the soul is in this thing, even though it doesn't have anything to do with me otherwise."

In their 2003 paper, Pierce et al. [32] define psychological ownership as "that state where an individual feels as though the target of ownership or a piece of that target is 'theirs'."

In the field of psychology, there have been numerous theoretical propositions and empirical studies attempting to explain the formation of psychological ownership. Several scholars have created frameworks based on decades of psychological research that capture key themes that have emerged time and again such as effectance and control of possessions [10, 25, 44], positive affect [10], and symbolic meaning and personhood [35].

Hegel's ideas of ownership stem from the notion that the "will" can be embodied in external entities, and that this embodiment is necessary for one's actualization as a person cannot come to exist without both relation to and differentiation from the external environment [34].

One of the most fundamental materialist theories is Locke's labor theory, which posits that "every man has a property in his own person," and thereby goes on to argue that when one mixes their labor with natural resources, the resulting good becomes their property - evoking the embodiment theory of personhood [22, 34].

Materialist theories stem from notions of property as control over material entities, going as far as to stipulate that physical, material states are the ultimate determinants of reality, taking precedence over thought, consciousness, and abstract entities [27, 38]. On the contrary, idealism posits that something mental is the ultimate foundation of reality, and idealist theories of property and personhood are concerned with symbolic and mental conceptions of ownership [12].

Building upon literature across psychology, philosophy, the humanities and social sciences more broadly, and within human-computer interaction, we introduce a nine-subdimension framework of creative ownership organized across Person, Process, and System. Person captures how the artifact relates to the self; Process characterizes the decisions, intentionality, and effort by which it is created; System situates creation within its material, collaborative, and contextual conditions.

Research on the self-creation effect illustrates how creating something oneself can lead to stronger object valuation and a more profound sense of ownership - aspects that are often overlooked by traditional frameworks of ownership. Therefore, we draw upon existing frameworks and approaches to produce a framework that is more streamlined for creative contexts.

In their 2003 paper, Pierce et al. define psychological ownership as "that state where an individual feels as though the target of ownership or a piece of that target is 'theirs'." In this paper, we will focus on a narrower definition revolving around creative ownership in which the target of ownership is a creative product or artifact that the individual in question had a role in creating — no matter how small or large.

In the field of psychology, there have been numerous theoretical propositions and empirical studies attempting to explain the formation of psychological ownership. Several scholars have created frameworks based on decades of psychological research that capture key themes that have emerged time and again such as effectance and control of possessions, positive affect, and symbolic meaning and personhood. These frameworks span a range of formulations ranging from Targets-Antecedents-Consequences-Interventions to corrective dual-process models, among others. Some of the major themes found across frameworks include responsibility, accountability, identity, self-efficacy, belongingness, control, self-congruity, psychological closeness, object-knowledge, self-investment, and rights over the object.

Hegel's ideas of ownership stem from the notion that the "will" can be embodied in external entities, and that this embodiment is necessary for one's actualization as a person cannot come to exist without both relation to and differentiation from the external environment. While the specifics of theories vary, the investment of one's self, values, and identity as a means of developing feelings of ownership is a common theme that arises.

One of the most fundamental materialist theories is Locke's labor theory, which posits that "every man has a property in his own person," and thereby goes on to argue that when one mixes their labor with natural resources, the resulting good becomes their property - evoking the embodiment theory of personhood. "Bundle of Rights" views hold ownership as a set of contractual obligations between people in relation to property.

While there are many schools of philosophical thought that could be used to frame a discussion of ownership, two juxtaposing ones that encompass the duality of ownership related values are materialism and idealism. Materialist theories stem from notions of property as control over material entities, going as far as to stipulate that physical, material states are the ultimate determinants of reality, taking precedence over thought, consciousness, and abstract entities. On the contrary, idealism posits that something mental is the ultimate foundation of reality, and idealist theories of property and personhood are concerned with symbolic and mental conceptions of ownership. This dualistic framing captures both the tangible and intangible elements of ownership.

Engineering refers to the use of technical principles, such as mathematics, science, and technical know-how, to realize a design that best meets a given set of expectations, which are typically captured in a requirements specification.

Designing is the process of arriving at a plan, specification, prototype, system, or service—a design. In HCI, this often means designing a user interface and relevant parts of the underlying interactive system.

HCI focuses on people who use an interactive system or are affected by its use. This focus is often called being user-centered or human-centered to contrast it with a focus on the technology itself [423, 604].

Finally, interaction often involves co-adaptation between people and computers [646], meaning that both the user and the system learn and adapt to each other during interactions.

Interaction is, in other words, not a property of the system design or the user but something that emerges when they influence each other.

The development of technology for interactive computing systems has been an important driver behind the widespread adoption of computing we have witnessed in the last 50 years.

In HCI, evaluation refers to the application of some systematic methodology to attribute human-related values to an artifact, prototype, system, or process. Examples of such attributes include performance, experience, safety, and ethical aspects, such as the avoidance of bias or harm.

Programmability lends computers their power as tools. Computer programs can decompose complex activities into sequences of much simpler operations.

A special part of a computing system is the user interface. It is the part that the user can see and utilize to control the computer. Through the user interface, users can provide input and instructions to a computer and receive feedback from it. In short, the user interface enables interaction with a computer.

In multitasking, tasks compete for limited sensory, motor, and central (cognitive) capacities

Visual objects that are unique in their visual primitives attract user's attention.

Interaction is a concept that is fundamental in HCI and specific to this field [357]. Intuitively, it refers to the reciprocal influence between people and an interactive system that takes place through the user interface.

Users continuously adapt their social behavior to compensate for the lack of social cues in computer-mediated communication

Users' performance in providing input to a computer is limited by a speed–accuracy trade-off

A mental model captures how people understand something. For instance, people have vastly different beliefs about how calculators work [598]. These beliefs can explain the errors and the issues they face when using calculators.

Interactive systems are tools that help users achieve their goals.

The remarkable efficiency, flexibility, and scalability of computers as tools boil down to the concept of a programmable machine capable of interpreting computer programs.

Programmability lends computers their power as tools.

A key technical construct in HCI is the user interface. It refers to the parts of an interactive system that the user comes into contact with or that in other ways shape the user's perception of the system.

In HCI, evaluation refers to the application of some systematic methodology to attribute human-related values to an artifact, prototype, system, or process.

A special part of a computing system is the user interface. It is the part that the user can see and utilize to control the computer.

Programmability lends computers their power as tools.

It is an egocentric fallacy to assume that others are like us—to attempt to explain other people by reference to one's own experience.

Agentic Coding is a Trap

The 2026 Global Intelligence Crisis

What 4 engineers with 10+ years of experience say about staying relevant in the AI era

Ask HN: What skills are future proof in an AI driven job market?

If most efficiency improvements came from a small handful of scale-dependent innovations, then existing models of the software intelligence explosion may be flawed.

On RE-Bench's five open-ended extension tasks, preserved failure traces in ARA accelerate progress, but can also constrain a capable agent from stepping outside the prior-run box depending on the agent's capabilities.

Tolerable for human readers, these costs become critical when AI agents must understand, reproduce, and extend published work.

We also learned that treating agents as rigid nodes in a state machine doesn't work well. Models get smarter and can solve bigger problems than the box we try to fit them in.

Our early versions of agentic work was only asking Codex to implement the task. That approach proved too limiting. Codex is perfectly capable of creating multiple PRs as well as reading review feedback and addressing it.

When our engineers no longer spend time supervising Codex sessions, the economics of code changes completely. The perceived cost of each change drops because we're no longer investing human effort in driving the implementation itself.

Among some teams at OpenAI, we saw the number of landed PRs increase by 500% in the first three weeks.

Six months ago, while working on an internal productivity tool, our team made a controversial (at the time) decision: we'd build our repo with no human-written code. Every line in our project repository had to be generated by Codex.

Instead of using domain knowledge to prescribe team organization, roles, or workflows, Fugu learns to dynamically assemble agents from a pool and coordinate them through non-obvious but highly efficient collaboration patterns.

He argues that specific algorithmic “cleverness” matters far less than the massive scaling of a few fundamental inputs

we are nearing the “end of the exponential” for AI development

The good world is where everyone has AI, and not as a revokable privilege through an API, but through hard possession.

In one case [first reported by the Financial Times](https://www.ft.com/content/00c282de-ed14-4acd-a948-bc8d6bdb339d?syn-25a6b1a6=1), an Amazon Web Service agent called Kiro purportedly decided the best way to upgrade a particular software service was to delete the whole thing and start over — and was able to do so without asking for human permission

Instead of just answering a user’s questions, the way a chatbot does, agents can take a human user’s instructions and act on them

We’ve seen remarkable adoption since its launch, with over 103,000 agents built and a total of more than 1.1 million agent sessions recorded

Military personnel and Defense Department civilians have used a version of Google Gemini’s [Agent Designer](https://docs.cloud.google.com/gemini/enterprise/docs/agent-designer) to create over 100,000 semi-autonomous AI agents in less than five weeks since the tool became available

We built AI into our editor's foundation instead of bolting it on top.

But there’s a critical difference between using agents to accomplish defined objectives and spinning up 20 agents because the dashboard makes you feel like a general commanding an army.

The average employee AI usage was 1.5 hours per week. The average CEO AI usage was less than one hour per week.

The enthusiasm has spawned an entire ecosystem of tools designed to make you feel like you’re running a company with AI agents.

37,000 lines per day. And this was the output.

Two prominent tech leaders, both publicly using the word psychosis. Both framing sleeplessness and obsessive agent usage as a feature of the moment rather than a bug.

It’s feeling like a new form of [AI psychosis](https://en.wikipedia.org/wiki/Chatbot_psychosis).

Anthropic says it has no way to control or shut down its AI models once they're deployed by the Pentagon

Bun operates its own fork of Zig, and recently achieved a 4x performance improvement on Bun compile after adding 'parallel semantic analysis and multiple codegen units to the llvm backend'.

Putting a leaderboard in place was always going to incentivize much more AI usage.

The rankings, set up by a Meta employee on its intranet using company data, measure how many tokens — the units of data processed by AI models — employees are burning through.

The rankings, set up by a Meta employee on its intranet using company data, measure how many tokens — the units of data processed by AI models — employees are burning through.

Employees at Meta Platforms who want to show off their AI superuser chops are competing on an internal leaderboard for status as a “Session Immortal”— or, even better, “Token Legend.”

Today’s LS guest, Mikhail Parakhin, CTO of Shopify, had another take on the “tasteful tokenmaxxing” - you want to go for depth (e.g. do more serial autoresearch loops) than go for breadth (e.g. solve a problem by kicking off 5, 10, 50, 500 parallel runs of the LLM slot machine). Worth thinking through.

the top conversations we have been hearing from AI leadership (CTOs, VPs, Founders) have all centered around the concept of “Tokenmaxxing” and how leaders want to get their teams using more AI, WITHOUT the downside of incentivizing the kinds of horrendous waste

AI News for 4/21/2026-4/22/2026. We checked 12 subreddits, [544 Twitters](https://twitter.com/i/lists/1585430245762441216) and no further Discords.

Endorsement reversal occurred in fewer than 3 in 1,000 observations.

AI systems currently provide more consistent fraud warnings than lay humans in an identical advisory role.

The AI-generated image of Neukgu had prompted Daejeon city government to issue an emergency text to residents, warning them of a wolf near the intersection.

The most urgent finding this week comes from researchers who demonstrated that the very mechanism enabling agents to use tools - function calling - can be hijacked with alarming reliability.

Nothing in between. A model that arrives at the correct answer through careful reasoning receives the same reward as one that guesses correctly by chance.

They deliver every answer with the same unshakable certainty, whether they're right or guessing.

And it’s not just the US putting chatbots at commanders’ fingertips; China is commissioning similar tools, according to recent [analysis] by Georgetown University’s Center for Security and Emerging Technology.

Today’s military personnel might give chatbots a list of potential targets to help decide which to strike first.

The transition from isolated AI models to the aggregated, metered token economy will unlock the twenty-first.

Consider the deep anatomy of an individual AI session to understand how this telemetry actually works in practice.

The smartest companies are no longer just hiring talent; they are purchasing synthetic intelligence by the gigawatt.

The smartest companies are no longer just hiring talent; they are purchasing synthetic intelligence by the gigawatt.

We do not publish AI-generated images, audio, or video as authentic documentation of real events.

Anyone who uses AI tools in our editorial workflow is responsible for the accuracy and integrity of the resulting work.

These standards have governed our editorial work since AI tooling became available.

We don’t publish claims based solely on AI-generated summaries, and reporters may not represent any material as “reviewed” unless they have examined it directly.

Ars Technica is written by humans. Our reporting, analysis, and commentary are human-authored.

We do not publish AI-generated images, audio, or video as authentic documentation of real events

Reporters may use AI tools vetted and approved for our workflow to assist with research

Our reporting, analysis, and commentary are human-authored

AI cannot replace human insight, creativity, and ingenuity

The company has also had to make major investments in its AI efforts in order to keep up with competitors in the space — earlier this month, it debuted a completely overhauled AI product called Muse Spark.

In truth, nate relied heavily on teams of human workers—primarily located overseas—to manually process transactions in secret, mimicking what users believed was being done by automation

Authoritarianism in all its forms depends on people acting against their own interests, in this case seeing the abdication of agency and giving up of rights as something positive, as almost a relief. Let the leader decide for you! Let the leader, who is an agent of divine providence, decide your destiny! Getting people to believe this is one of the main functions of personality cults that depict the leader as infallible.

The LLM took an entirely different route, using a formula that was well known in related parts of math, but which no one had thought to apply to this type of question.

They range dramatically in both significance and difficulty, and many AI solutions have turned out to be less original than they appeared.

The LLM took an entirely different route, using a formula that was well known in related parts of math, but which no one had thought to apply to this type of question.

But experts have warned that these problems are an imperfect benchmark of artificial intelligence's mathematical prowess. They range dramatically in both significance and difficulty, and many AI solutions have turned out to be less original than they appeared.

We have discovered a new way to think about large numbers and their anatomy. It's a nice achievement. I think the jury is still out on the long-term significance.

Liam Price just cracked a 60-year-old problem that world-class mathematicians have tried and failed to solve. He's 23 years old and has no advanced mathematics training.

Liam Price just cracked a 60-year-old problem that world-class mathematicians have tried and failed to solve. He's 23 years old and has no advanced mathematics training.

I had the intuition that these problems were kind of clustered together and they had some kind of unifying feel to them. And this new method is really confirming that intuition.

The LLM took an entirely different route, using a formula that was well known in related parts of math, but which no one had thought to apply to this type of question.

Resolution increases make them more expensive, then efficiency gains reduce costs - a sawtooth pattern.

Will smarter models be increasingly expensive because of greater accuracy or less expensive because they're smarter?

Smaller pieces force the model to pay closer attention to each word, like reading a contract word by word instead of skimming paragraphs.

Then Opus 4.7 shipped & the smarter model became much more expensive. The cause : a new tokenizer

Opus 4.5 costs 67% more than Sonnet. But Opus 4.5 used 76% fewer tokens to reach the same outcome.

When Anthropic launched Opus 4.5 in November 2025, the bigger, more expensive model was actually cheaper to use.

The agent interprets new information and adapts the logic. The engine applies that logic continuously and emits precise updates.

Agents and CDC streams are powerful together because they split the work well.

The fix is not smarter prompts. It is software built to meet agents halfway.

Today's agents, the copilots, the chatbots are designed to be human like.

We have always been wary of AI generated code, but felt everyone is free to do what they want and experiment, etc.

This ultimately also leads to false positives, but my manual QA run verified it's maybe 5-10%.

LLM tend to use certain font combos like Space Grotesk, Instrument Serif and Geist

Claude Code has led to a large increase in Show HN projects. So much, that the moderators of HN had to restrict Show HN submissions for new accounts.

I guess people will get back to crafting beautiful designs to stand out from the slop. On the other hand, I'm not sure how much design will still matter once AI agents are the primary users of the web.

Is this bad? Not really, just uninspired. After all, validating a business idea was never about fancy design, and before the AI era, everything looked like Bootstrap.

A designer recently told me that 'colored left borders are almost as reliable a sign of AI-generated design as em-dashes for text'

The good world is where everyone has AI, and not as a revokable privilege through an API, but through hard possession.

It works for Mars. I think there's so much value in colonizing Mars, and it's sad to me to see SpaceX diluting the mission buying up random AI bubble crap.

Even the ideal version, industrial megaprojects at hyperhuman scale while constantly being out over your skis with leverage sounds hellish.

Three of the four metrics (ECI, log METR 50% time horizon, and a math-focused index we constructed from several math benchmarks) show strong evidence that progress has sped up relative to a global linear trend fit to data from 2023 onward.

Three of the four metrics (ECI, log METR 50% time horizon, and a math-focused index we constructed from several math benchmarks) show strong evidence that progress has sped up relative to a global linear trend fit to data from 2023 onward.

Several correlated but not strictly identical changes happened over the same few months: scaling inference compute, heavier use of RL in post-training, and models producing reasoning tokens.

Tasks where correctness is harder to verify may not have seen the same speedup, so the acceleration we document here may not be as general as the headline numbers suggest.

The three metrics where we find acceleration are concentrated in programming and mathematics. These are areas that labs have explicitly targeted for improvement, and they share an important property: correctness is easy to verify automatically.

Three of four metrics show strong evidence of acceleration, seemingly driven by reasoning models.

The result is a mismatch that should terrify anyone building software: the attack surface is expanding faster than any human can monitor, and the entities making dependency decisions are increasingly not human.

Ask ten different programmers how they use AI, and you can get ten different answers.

over one million Trainium2 chips to train and serve Claude

Amazon is investing $5 billion in Anthropic today, with up to an additional $20 billion in the future. This builds on the $8 billion Amazon has previously invested.

Anthropic will also use incremental capacity for Claude in Amazon Bedrock. The agreement includes expansion of inference in Asia and Europe to better serve Claude's growing international customer base.

Our run-rate revenue has now surpassed $30 billion, up from approximately $9 billion at the end of 2025.

We have signed a new agreement with Amazon that will deepen our existing partnership and secure up to 5 gigawatts (GW) of capacity for training and deploying Claude

up to 5 gigawatts (GW) of capacity for training and deploying Claude

Testing universal jailbreaks for biorisks in GPT‑5.5

As part of this investigation, we ran more ablations (removing lines from the system prompt to understand the impact of each line) using a broader set of evaluations. One of these evaluations showed a 3% drop for both Opus 4.6 and 4.7.

We reverted this change on April 7 after users told us they'd prefer to default to higher intelligence and opt into lower effort for simple tasks.

Anthropic made fun of this idea during the last Super Bowl.

The network requirement is only for the initial download of the model. Subsequent use of the model does not require a network connection. No data is sent to Google or any third party when using the model.

I would put venture capitalist in finite demand & open loop. There's only a certain amount of venture capital dollars entering the ecosystem in a year, & investment selection remains an open problem.

Open Loop + Finite Demand = Utility Tools. Preparing 10-Ks & 10-Qs. Legal contract review. Insurance claims processing. One report per quarter, one contract per deal. AI makes the work faster, but doesn't create new work to do.

Closed Loop + Infinite Demand = Economic Engines. Software engineering lives here. AI writes the code. Tests verify correctness. More code enables more features. Companies will always need more software.

The compliance-driven buyers improvising local AI out of retail Mac Minis because the product they need does not exist.

Why the company that moved computing off the mainframe fifty years ago is making the same structural move with AI, and what that predicts.

The question it forces is not which model is best. It is who owns the inference layer your organization depends on, what happens when the economics of that layer stop being subsidized, and whether the thing in your pocket turns out to matter more than the thing in the datacenter.

The structural cost problem in AI inference that makes Apple's on-device bet defensible, not just defensive.

This means that improvements on SWE-bench Verified no longer reflect meaningful improvements in models' real-world software development abilities. Instead, they increasingly reflect how much the model was exposed to the benchmark at training time.

Our RL infra team used a K2.6-backed agent that operated autonomously for 5 days, managing monitoring, incident response, and system operations, demonstrating persistent context, multi-threaded task handling, and full-cycle execution from alert to resolution.

The architecture scales horizontally to 300 sub-agents executing across 4,000 coordinated steps simultaneously, a substantial expansion from K2.5's 100 sub-agents and 1,500 steps.

Kimi K2.6 autonomously overhauled exchange-core, an 8-year-old open-source financial matching engine. Over a 13-hour execution, the model iterated through 12 optimization strategies, initiating over 1,000 tool calls to precisely modify more than 4,000 lines of code.

Kimi K2.6 demonstrates significant improvements over Kimi K2.5 in internal evaluations conducted by CodeBuddy: code generation accuracy increased by 12%, long-context stability improved by 18%, and tool invocation success rate reached 96.60%.

Meta founder and CEO Mark Zuckerberg described superintelligence in a blog post last year

Claude is now being deployed to NEC Group employees around the world

NEC will establish a Center of Excellence to develop a highly skilled, AI-enabled engineering organization

As part of its long-running Client Zero initiative, in which NEC serves as its own first customer before offering its technology to clients

NEC aims to build one of Japan's largest AI-native engineering teams, who will use Claude Code in their work.

Our most complex pages, which took 20+ prompts to recreate in other tools, only required 2 prompts in Claude Design.

Claude Design gives designers room to explore widely and everyone else a way to produce visual work.

GPT‑5.5 found a proof of a longstanding asymptotic fact about off-diagonal Ramsey numbers, later verified in Lean. The result is a concrete example of GPT‑5.5 contributing not just code or explanation, but a surprising and useful mathematical argument in a core research area.

We are treating the biological/chemical and cybersecurity capabilities of GPT‑5.5 as High under our Preparedness Framework. While GPT‑5.5 didn't reach Critical cybersecurity capability level, our evaluations and testing showed that its cybersecurity capabilities are a step up compared to GPT‑5.4.

Losing access to GPT‑5.5 feels like I've had a limb amputated.

GPT‑5.5 delivers this step up in intelligence without compromising on speed: larger, more capable models are often slower to serve, but GPT‑5.5 matches GPT‑5.4 per-token latency in real-world serving, while performing at a much higher level of intelligence.

OpenAI pledged $1.5B to a joint venture called DeployCo, guaranteeing private-equity partners a 17% annual return floor over five years.

Jeremy didn't get laid off. He got leveraged.

The Meta cuts are the inverse. When one person with the right AI tools can do the work of 10-to-15 people, the person most at risk isn't the one using the AI. It's the one whose job description overlaps with what AI now does by itself.

A US lab would never; well, unless you count a code red or Meta's throw money at the problem moves.

Our alignment assessment concluded that the model is 'largely well-aligned and trustworthy, though not fully ideal in its behavior'. Note that Mythos Preview remains the best-aligned model we've trained according to our evaluations.

On some measures, such as honesty and resistance to malicious 'prompt injection' attacks, Opus 4.7 is an improvement on Opus 4.6; in others (such as its tendency to give overly detailed harm-reduction advice on controlled substances), Opus 4.7 is modestly weaker.

Opus 4.7 is better at using file system-based memory. It remembers important notes across long, multi-session work, and uses them to move on to new tasks that, as a result, need less up-front context.

Interestingly, this means that prompts written for earlier models can sometimes now produce unexpected results: where previous models interpreted instructions loosely or skipped parts entirely, Opus 4.7 takes the instructions literally.

For Anthropic, more usage across diverse tasks means more data, which produces a smarter model—just as more queries improved Google search.

A free, good-enough product is enough to change market dynamics.

The risk of this strategy to the ecosystem is that it makes previously attractive categories no longer viable.

The commoditization flywheel : both companies give away complements to drive usage of the core.