Structural Mapping Theory (SMT) is a long-standing well-vetted theory from Cognitive Science that describes how humans attend to and try to compare objects by finding mental representations of them that can be structurally mapped to each other (analogies).

These examples of text-centric lossless techniques do not abstract away or summarize; they strategically re-organize and re-render the existing text to help enhance readers' own perceptual cognition, informed by Structural Mapping Theory (SMT) [17].

This SMT-informed approach, which AbstractExplorer shares, tries to give this mental machinery “a leg up,” letting users perhaps skip some steps by accepting reified cross-document relationships identified by the computer.

The human perceptual, comparative mental machinery that SMT describes is part of what enables humans to form more abstract structured mental models from concrete examples, among other critical knowledge tasks.

AbstractExplorer instantiates new minimally lossy2 SMT-informed techniques for skimming, reading, and reasoning about a corpus of similarly structured short documents: phrase-level role classification that drives sentence ordering, highlighting, and spatial alignment.

Theory (SMT) to facilitate seeing both the overview and the details at the same time, facilitating abstraction without losing context.

With datasets like LOCUS we’re going to make the strange half-seen rules and laws that govern much of civic, local life be made accessible to AI systems, which may eventually allow them to better adapt themselves to hyperlocal purposes.

Gemini Omni and Nano Banana 2 Lite use [SynthID](https://deepmind.google/blog/identifying-ai-generated-images-with-synthid/) watermarking. You can verify AI content through the Gemini app, Gemini in Chrome or Search.

Markdown became the default AI output under three assumptions. All three made sense in 2022. None of them hold in 2026.

Premise 1: Humans hand-edit content. Markdown was designed for people who write and revise their own text. That’s how blogs, docs, and READMEs still work. But agent output is different. You send a prompt. The agent generates a 2,000-word analysis, a code review, a project plan. You read it, maybe share it. You almost never open it in an editor and start rewriting paragraphs. The format’s core value proposition — easy to edit by hand — no longer matches the use case.

Premise 3: Output is read-only. The old workflow was linear: prompt, generate, read, close. But the agent era is pushing toward something different. Users want to interact with the output: filter a table, adjust parameters, compare options side by side, export a subset, feed the result back into the next prompt. Markdown can’t carry interaction. It’s a one-way street.

From 8 years down to 6 months: How we built AI to split the monday.com monolith

For decades, code contributions have been how open source projects learned who to trust. People would show up, do the work, take responsibility for their changes, and stick around. Over time, trust emerged from the work itself. AI tools have changed the economics of this very quickly. We use them ourselves every day, but a pull request no longer tells us as much as it used to about the person submitting it. A substantial patch used to imply substantial effort, and that effort was a reasonable proxy for good faith. That assumption no longer holds. For a browser, this matters. A browser runs untrusted input from the entire internet on the user’s machine, and one well-disguised vulnerability is all an attacker needs. We have already seen patient, well-resourced campaigns in open source to earn maintainer trust and abuse it. What has changed is how much faster and cheaper it has become to produce work that looks like a serious contribution.

Both Scarlata and Gingras are concerned that papers by less prominent scientists have disappeared as well without anyone realizing. At a minimum, Gingras wants Planck’s papers restored. “Whoever did it, I don’t care,” he says, “just put them [back] in the database. Intellectually, it’s not acceptable.”

you can't produce the logic using the local files. The reasoning logs on your system are not accessible to you.

Getting the full thinking output requires an enterprise agreement.

Claude encrypts its reasoning into that signature. Anthropic holds the key. Your machine doesn't receive it.

SpaceX is reportedly in talks to merge with xAI

Orbital data centers are the most efficient way to meet the accelerating demand for AI computing power

Data access inhibits independent research into hiring algorithms

We conduct the largest empirical study of algorithmic hiring with data for 3.4 million real job applicants submitting 4 million applications to 156 employers across 11 market sectors.

Over 90% of U.S. employers rely on hiring algorithms to screen job applicants.

The functionality seamlessly supports everything from basic arithmetic to highly intricate calculations, simplifying what is traditionally a frustrating and time-consuming debugging process.

When you encounter a formula error, Gemini can analyze the surrounding data structure to help provide an easy-to-understand explanation of the core issue alongside a corrected version of the formula.

Serves as the first generative core for social world models, a foundation for next-generation AI-native social platforms.

The Maia 200 does beat the B300 in efficiency, however, a big win in a day where public opinion against AI's environmental effects is steadily mounting. The Maia 200 operates at almost half of B300's TDP (750W vs 1400W)

Recent events highlight how important open source is to the AI ecosystem, with more nations and enterprises recognizing the risks and costs associated with exclusively depending on closed models.

For SpaceX, the deal is another sign that compute itself has become strategic currency in the AI race.

Reflection has leaned directly into that pitch as the startup, last valued at $25 billion, is trying to build American open-source AI models that can compete with frontier systems from OpenAI, Anthropic and Google.

The deal shows how SpaceX is using its massive data center build-out after its record initial public offering.

The models are finally ready. Costs of inference are getting optimized with open models, and even on-device models.

we can finally invent new products that allow users to do things more naturally, using simple language to express their needs.

when I first experienced OpenClaw earlier this year, I had the epiphany that it isn't the models that matter, but the harnesses, loops, and context which will lead to so many new opportunities ahead.

Include AI-generated sexualized impersonation as a separate category in standard content reporting and appeal forms, distinct from 'harassment' or 'nudity.'

Meta said that when the content was flagged, the company had no indication that the individual depicted in the video was 'a real person' because they did not report the content.

The Board finds that AI-generated impersonation is non-consensual by default and should be added to the set of signals the company uses to establish lack of consent.

We would like to thank Deepseek-OCR, Deepseek-OCR-2, PaddleOCR for their valuable models and ideas.

The NVIDIA DSX reference design for AI factories has zero water consumption — we have eliminated massive amounts of power usage and pretty much all water usage.

Raw output quality is on par with top frontier models, but Fugu showed unusually strong persona stability across long sessions, holding its identity where other models drift.

Collective intelligence serves as the practical hedge against this concentration of power.

orchestration is no longer just a technical optimization; it has become a geopolitical and operational imperative.

the most powerful AI systems will not be isolated monoliths, but collaborative ecosystems.

AI may generate an insight, but people must still evaluate its significance and plausibility.

That was the moment that I felt like, okay, these models have now come to a point where they really, truly understand.

helps sustain progress across long-running projects

break ambitious goals into verifiable steps

determine when to delegate execution to Codex versus when human oversight is most valuable

How Codex helps work continue beyond a single prompt

Security engineers reviewed every finding before it reached a maintainer... While frontier AI models are highly capable of finding vulnerabilities and patching them, they also produce a high volume of false positives

Trail of Bits engineers found that, with limited guidance, GPT‑5.5‑Cyber made useful choices about where to expand coverage, which builds and entry points to probe, and which candidates were too weak to pursue.

Async agents are moving into everyday work. For an agent to be trustworthy and useful inside an organization, it needs real enterprise data: CRM records, repositories, inboxes, knowledge bases.

Models building their own software tools might have seemed outlandish not long ago, but it is happening. It would be unwise to rule out the same trajectory in hardware.

We are plausibly entering the early era of physical agentic AI.

This progress is not the result of a concerted effort to improve the robotics capabilities of our models. These improvements, like so many others in the history of LLM development, have emerged from much more general scaling.

Claude Opus 4.7—operating without human assistance—was about 20 times faster than the fastest human team at all tasks completed by our participants less than a year ago.

Claude can even automatically learn from _other_ Slack channels and data sources, if it's granted permission.

We now spend much more of our time delegating tasks to many Claudes in parallel.

Today, 65% of our product team's code is created by our internal version of Claude Tag.

Qualcomm Dragonfly AI300 joins the previously announced Qualcomm Dragonfly AI200 and AI250 in its data center solutions portfolio with an annual cadence AI accelerator roadmap

HBC is designed to enable efficient scaling of AI agents to meet the demands of continuous reasoning, memory bandwidth, and real-time responsiveness

AI300 with HBC Gen 2 is designed to enable another stepwise improvement with a 54x increase over AI200

HBC is designed to enable a 6x increase in bandwidth per watt versus HBM compared to competing published product specifications normalized at card-level

Memory prices have skyrocketed in the last couple years as AI chips eat up all the production capacity of the small crop of vendors.

The goal is to move beyond using models to find more vulnerabilities, towards a world of safer software and cyber resilience.

As AI makes it possible to find and patch more vulnerabilities faster, it also creates more work for maintainers, who need to sift through thousands of reports, many of which are low-quality false positives.

The bottleneck historically has been finding vulnerabilities, but now defenders are overwhelmed with the number of vulnerabilities found. Instead, the bottleneck is now patching vulnerabilities.

Public reaction on the ClaudeAI subreddit appears to be split into roughly three camps. The majority see the story as an indictment of the government's cybersecurity, citing its inability to hire the required level of talent and its history of leaks. A second large group is skeptical of the claim, considering it sensationalist or even an Anthropic marketing stunt.

The Financial Times reported earlier in June that roughly six Anthropic engineers are embedded directly inside the agency as forward-deployed staff, adapting and customizing Mythos for specific operational applications, with sources indicating the work could extend to infiltrating networks operated by countries including China and Iran.

Anthropic contends that the cited breach was a narrow jailbreak, one that rival models, including OpenAI's GPT-5.5, also exhibit. According to the company, the flagged behavior amounted to asking the model to analyze a codebase and fix identified issues, which revealed a few minor, already known bugs, rather than a genuine autonomous offensive intrusion.

The story sheds light on the June 12 U.S. government directive barring all foreign nationals, including Anthropic's own non-citizen employees, from accessing the Fable 5 and Mythos 5 models, citing national security concerns.

HappyHorse is built around a 15-billion-parameter unified self-attention Transformer that processes text, image, video, and audio tokens within a single token sequence. Unlike many competitors that stitch together separate models for video and audio

OpenAI's Sora web and app experiences were discontinued on April 26, with the Sora API set to follow on September 24. The shutdown came after the product proved financially untenable: Sora cost roughly $1 million per day to operate but generated only about $2.1 million in total revenue

Only the iPhone Air, iPhone 17 Pro, and the iPhone 17 Max will have all the fixings, like more varied voice options. As for the rest of the lineup: Every iPhone 16 and iPhone 17 model will be able to run the new Siri, while only the iPhone 15 Pro and Pro Max will be compatible.

At WWDC 2026, Apple repeatedly referenced its privacy-preserving approach to Siri AI. As part of the company's Private Cloud Compute, Apple claims it doesn't store data from users and only pulls from it when you ask Siri a question.

Unlike the ChatGPT or Claude app, Siri AI is woven right into the iPhone, so it's even more ready to go beyond answering questions and start automating more aspects of the user experience.

Do you feel that the risks to an event like this are seriously compounded with the progress being made towards fully functional quantum computing?

I have worked in AI on clinical research trials and can see (even from my area in biology based AI research) that the world must not have a Chernobyl moment.

The AI arms race between China and the US has researchers on both sides worried about a "Chernobyl moment."

The AI arms race between China and the US has researchers on both sides worried about a "Chernobyl moment."

The cutbacks take place not long after Accenture threatened that employees would 'risk losing out on promotions' if they didn't use AI, 404 writes.

The cost of tokens has thrown into doubt the AI business model — as evidenced by what's being called the 'AI selloff' which has battered some AI-dependent businesses the last few days, especially memory chip makers.

The AI industry has reached the stage where it can't just be exciting and new anymore. It has to prove its worth.

The cost of tokens has thrown into doubt the AI business model — as evidenced by what's being called the 'AI selloff' which has battered some AI-dependent businesses the last few days, especially memory chip makers.

We now appear to be entering the era of token rationing.

The letter lands two months after the White House Office of Science and Technology Policy issued a memorandum that pledged to help AI companies detect and coordinate against industrial-scale distillation.

Anthropic said operators affiliated with Alibaba and its AI lab carried out 28.8 million exchanges with its models using roughly 25,000 fraudulent accounts between April 22 and June 5.

Anthropic sent a letter to U.S. officials accusing Alibaba of 'brazenly' and 'illicitly' attempting to extract its AI capabilities.

Last week, legendary AI researcher Noam Shazeer announced that he was leaving Google for OpenAI. Shazeer had been at Google since 2000, save for the three years he spent building his controversial chatbot startup, Character.AI.

Gemini already excels at function calling and using built-in tools like Search and Maps grounding. With built-in computer use capability, developers can now use 3.5 Flash to reliably build custom agents that can see, reason and take action across browser, mobile and desktop environments.

Previously only available as a standalone Gemini 2.5 computer use model, computer use is now integrated natively in the main Gemini Flash model.

Computer use is now a built-in tool supported in Gemini 3.5 Flash, delivering our best performance yet for agentic computer use tasks.

Researchers have done rich historical investigations of individual notations (e.g., [3, 70, 74, 76, 115]), but the more general mechanisms and patterns through which new notations are created and formalized are less understood.

We began with a diverse set of notations across five disciplines—music, dance, chemistry, physics, and computer programming—that had prior historical literature to draw upon in our initial analysis.

Since histories of specific notations tends to miss detailed, direct observations around the initial creation process, we complement this "macro" analysis with occasional references to experiment-based literature from experimental semiotics, communication theory, and cognitive science into how people use notations to ground communication, largely in lab studies.

we conducted a comparative historical analysis of the development of different notations which individually have been documented in prior literature. Specifically, we conduct a parallel comparative history which "seek[s] above all to demonstrate that a theory similarly holds good from case to case... [and where] differences among the cases are primarily contextual particularities against which to highlight the generality of the [theorized] processes"

Studying software teams, Cherubini et al. [34] found a "tendency to adopt informal, ad-hoc notations" and a "limited adherence to standards of any sort."

Studies are also conducted on various existing notating practices, usually in specific domains (e.g., how programmers draw diagrams to communicate ideas [34, 62, 63]).

What seem today as obvious notations often have relatively short histories: for instance, arrows in diagrams emerged around the 18th century.

Notations are deployed and embedded throughout the process of HCI and software development.

Almost everything we do with computers involves notations.

These informal interactions can then lead to formal representations, but depend upon pre-existing formalisms known to both humans and AI.

Seemingly 'obvious' notations to academics are also not obvious to everyone: e.g., about one-third of the U.S. and German populations have low literacy in reading data visualizations [51].

As current AI technologies rely upon, reproduce, and amplify established, dominant, already-formalized abstractions and notations in order to function

Many notations are culturally learned and inherited.

From our analysis, we derive a set of initial implications for the design of future systems that create new abstractions (Section 5), including that notations primarily originate through linking metaphors and most often in a social—rather than a technical—context, and that notation design decisions around what to include as "meaningful" (and thus what to exclude) are often left implicit by inventors, but could be made explicit and become manipulable objects through reification [10].

Our work contributes to a longstanding dream of dynamic abstractions in HCI, where users can dynamically communicate and express themselves through notations (interfaces) that they are most comfortable with at the moment of expression, beyond ones predefined by developers [96, 143, 144, 148, 149].

Here we present suggestions for system designers, with concrete examples inspired by our patterns. These are just some interesting ideas that came to mind, rather than an exhaustive list.

Alongside the social stages of notation development above are three functional stages that emerge from reflection upon our analysis—descriptive, generative, and evaluative stages (borrowing terminology from Generative Theories of Interaction [11])

Our historical analysis suggests that, cognitively and socially, a notation proceeds by: (1) Enumerating dimensions of meaningful variation in the target domain, which proliferate as more situations are encountered or considered (whether by inventors or users) (2) Mapping dimensions of meaningful variation to perceptual channels of representation (3) Designing the notation to leverage perceptual affordances by visual analogy to embodied transformations like pouring cups or rotating shapes, and ensuring these "natural" manipulations hold meaning in the target domain

These stages form a spectrum and are not rigid boundaries. We clustered patterns into the most relevant stage for ease of presentation; however, patterns can be applicable across stages.

Our review identified many empirical patterns in the notation development process. We state each pattern, briefly describe it, and provide examples.

Our analysis identifies 33 patterns of how notations are created, evolved, and formalized over time, which are largely shared across histories and loosely categorized into three social stages of development (invention/incubation, dispersion/divergence, and institutionalization/sanctification) and three functional stages (descriptive, generative, and evaluative).

What about novel formalisms and notations? How are new abstractions created, evolved, and incrementally formalized over time—and how might new systems, in turn, be explicitly designed to support these processes?

How might we co-create a new notation with a machine, and thereafter communicate through that notation, even share out the notation to broader communities?

While current AI systems support "horizontal" translations from informal ideas to established notations, how should we ensure that the "vertical" process of creation—new notations, new abstractions—is also supported?

How do humans ultimately develop new notations, new formalisms, and new abstractions, that they use to communicate with machines and each other?

The use of notation happens everyday in small ways, e.g., whenever people work together over a whiteboard or paper towards a joint objective. People jot down X's, boxes and arrows to stand-for concepts they are working through.

Human-computer interactions have historically been mediated by formally-defined structures—such as command-line interfaces, graphical user interfaces, and programming languages—that provide an unambiguous mapping to an underlying formal model.

SDT broadly differentiates three types of motivation [157]: Intrinsic motivation denotes activity pursued for its inherently interesting or enjoyable qualities. Extrinsic motivation refers to activity pursued for a separable outcome. Amotivation denotes the absence of intentional motivation, where a person may no longer be aware why they pursue an activity.

Basic psychological needs theory (BPNT) posits three basic psychological needs that energise organismic processes: competence, the feeling of having an effect; autonomy, a sense that actions are self-endorsed and performed willingly; and relatedness, a sense of reciprocal care, value, and belonging in relation to other social figures and collectives [158].

SDT is broadly organised into six mini-theories, whose underlying concepts are continuously developed, critiqued, and revised (e.g., [186, 190, 191]).

At its core, SDT is a scientific theory [163], in that it contains a number of empirically-testable propositions [199] that generalise across varied contexts, which serve to explain and predict the impact of certain events on motivation and wellbeing.

SDT is a psychological macro-theory of human motivation, growth, and wellbeing [47, 48, 163] that characterises humans as fundamentally active organisms.

Self-Determination Theory (SDT), a major psychological theory of human motivation, has become increasingly popular in Human-Computer Interaction (HCI) research on games and play.

To our knowledge, the first SDT research involving videogames [18] was conducted shortly after Deci's original formulation of CET [129] and investigated whether extrinsic rewards would reduce intrinsic motivation even for 'highly intrinsically motivating' activities such as videogame play. Videogames' intrinsically motivating qualities were also examined in early research on learning [e.g., 351]; however, focused examination of other core SDT concepts such as need satisfaction largely began much later [365].

Research on games and play in HCI (henceforth HCI games research), however, has continued to employ broad psychological theories as foundational work [417, 556]. One prominent example can be seen in self-determination theory (SDT) [481, 483], an influential theory of human motivation, which has provided HCI games research with propositions and concepts that can help explain motivational and experiential qualities of games and game-adjacent systems (e.g., gamification).

Psychological concepts and models have long been employed in human–computer interaction (HCI) to theorise the human user [88]. However, early applications of cognitive psychological theory did not develop into a coherent foundation of knowledge about human factors [89, 109, 455]—circumstances that Rogers [456, p. 22] attribute to "the stark differences between a controlled lab setting and the messy real world setting" for which interactive artefacts and systems are designed. The deployment of broad theory in HCI has subsequently declined in the intervening years [455, 456], and this sporadic progress in theory development in domains such as usability and user experience (UX) has been identified as a cause for concern [249, 314].

GLM-5.2 vs Claude Opus

AI napędzi polską gospodarkę, ale są też koszty. Grubo ponad ćwierć miliona osób może stracić pracę

Anthropic has not had the best relationship with the Trump administration in a way that stands apart from the other leading AI labs

This historic deployment for OpenAI is particularly significant because Samsung Electronics, a global leader in technology and manufacturing, is embracing AI not as a tool limited to certain teams or functions, but as a core platform for improving how employees around the world work and innovate.

AI Is Slowing Down

Leaked financial docs show OpenAI is losing billions of dollars a year

Prof. Geoffrey Hinton

Prof. Max Welling

While nature took billions of years to perfect molecules, we are harnessing AI to unlock trillion-dollar materials breakthroughs in months, not millennia.

these atoms of knowledge live in a variety of highly fragmented systems

Chemists found the suggestion both surprising and interesting

A key through line of all these tasks is that they are time consuming

Comparing agentic Qwen3.6 35b to Claude Opus is like a junior with knowledge across the board, that you really need to guide, versus a senior that thinks with you on architecture.

The nuances of tuning the carburetors & the timing belts of these complex beasts are tasks better assigned to a few vendors to deliver maximum intelligence per dollar & amortize the costs across a broader population.

Loops, the critical problem-definition exercise of this era, are hard to design. Systems design is an entire discipline... What is the best way to define a loop so an agentic system improves?

AI applications present three new disciplines to master: picking the right models, developing the hill-climbing loop, & evaluating the performance of the system for each company

the Fable retraction exposed model dependency risk, Satya's thesis defined the learning loop, & Salesforce's $3.6B Fin acquisition priced the harness.

The government believes it has become aware of a method of bypassing, or 'jailbreaking' Fable 5.

Security experts say that can't be done.

Trump administration officials tell WIRED that if Anthropic wants to rerelease Fable 5, it will need to ensure the model's guardrails can't be circumvented.

We believe the government should have the ability to block unsafe deployments, as part of a statutory process that is transparent, fair, clear, and grounded in technical facts.

We have found that other publicly-available models are able to discover them as well without requiring a bypass.

If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers.

We suspect that perfect jailbreak resistance is not currently possible for any model provider.

We suspect that perfect jailbreak resistance is not currently possible for any model provider.

We have found that other publicly-available models are able to discover them as well without requiring a bypass.

Running local models is good now

xAI struck a deal to give Cursor access to its compute infrastructure, foreshadowing similar, larger deals with Anthropic and Google in the future.

This is a marriage between two companies that have arguably been falling behind in the AI race.

By handling the specific invalid behavior instead of rejecting the entire trajectory, this approach helps prevent the training instability and model collapse that can happen when rollouts are abruptly stopped.

As a limited-time promotion through the end of September, off-peak usage is billed at 1×. (Peak hours are 14:00–18:00 UTC+8 (Beijing Time) daily).

We find that GLM-5.2 shows more potential hacking behavior than GLM-5.1. This makes the verification signal easy to optimize, but fails to actually improve the fundamental capabilities of the model.

On Terminal-Bench 2.1 (81.0) it lands within a few points of Claude Opus 4.8 (85.0) — while staying ahead of Gemini 3.1 Pro.

GLM-5.2 is the highest-ranked open-source model, showing that its 1M context has translated into practical long-horizon delivery capability.

each prompt the user sends sets off a chain of around 10 actions taken by Claude on average

people make about 70% of the planning decisions but only 20% of the execution decisions

each prompt the user sends sets off a chain of around 10 actions taken by Claude on average

people make about 70% of the planning decisions but only 20% of the execution decisions

the message is clear: The AI industry isn't immune from U.S. government interference

The AI industry isn't immune from U.S. government interference

The US government's Anthropic models ban was never about an AI jailbreak

Restoring global trust in American AI is another thing entirely. No matter how long the shutdown lasts, it shined a light on how fragile access to US frontier AI models is.

Trump may see restricting Mythos and Fable as a matter of national security. But the argument cuts both ways, and with Washington now asking if AI is too important for everyone to have access, other governments are asking whether they can afford for Washington to decide who does.

Most governments and businesses cannot come close to matching the scale and resources of frontier labs in the US or China. But sovereign AI does not always mean building the biggest or the most powerful tools.