Whether these changes ultimately benefit the field or have a destructive effect will in large part be determined by the decisions of the humans in control of this new technology.

We are witnessing a general threat to intellectual work, with misalignment between the outcome of the use of AI and its initial purpose.

Without the willing mathematicians who must take care of their development and integration into the mathematical canon, AI-conceived ideas would never become fully alive and the crucial human transmission chain between mathematicians would be lost.

Often these solutions are announced in a rush, leaving no time for a proper writeup, the isolation of new methods and ideas, and citing relevant previous work of others.

The goals of the AI companies and the goals of the mathematical community are severely misaligned.

Over the last few months, the mathematical capabilities of LLMs have improved dramatically, to the point that they can solve major outstanding problems in many fields of mathematics.

I am proud to be among the list of 25 initial signatories — all Fields Medallists — to the declaration below

Clearview AI Is Testing an AI Tool That Would Let Cops Unearth Your Life Online

Gen AI Tools Are Now Being Used to Push 'Slop Jihad'

A new generation of Islamic terror supporters are using AI to spread 'Slop Jihad' to new audiences on TikTok.

As people increasingly turn to AI to discover products, compare options, and make decisions, we're creating new ways for people and businesses to connect.

Advertisers can now use simple, natural-language prompts to create, update, and analyze campaigns directly in ChatGPT with the Ads Manager plugin.

All audio generated by our AI products is watermarked with SynthID. This imperceptible watermark is woven directly into the audio output, ensuring AI-generated content remains detectable to help prevent misinformation.

Gemini 3.8 Live Extended Thinking provides enterprise-grade task completion and intelligence, capturing the #1 overall spot on Artificial Analysis' Speech to Speech Quality Index (82.6), and leads in agentic task completion with 68.6% on τ-Voice and 35.1% on Sierra's τ-Voice-banking benchmark.

The future we seek requires materials no one yet knows how to make. Our labs are learning how.

We benefit as frontier AI models improve, but when they fall short or become too costly, we train our own.

Our thesis is that experiments from our high-throughput labs provide the data to train increasingly capable scientific AI, which, in turn, guides better experiments.

We are extending this approach to train our AI to direct scientific campaigns, to develop synthesis procedures, and to decide which experiments to run.

learning from physical environments presents new issues: the number of agents is not elastically scalable (increasing the number of concurrent experiments requires power, equipment, and engineering), each experiment can take days, and the results are often ambiguous.

Periodic Neon is an early example: using our lab data, we trained a trillion-parameter model that outperforms GPT-6 Astra on a critical scientific analysis task using relatively little compute.

For an AI company looking to prove that its models are the best at mathematics, then, they are irresistible targets. To Buckmaster and many other mathematicians The Verge spoke to, that helps explain why OpenAI moved so ferociously when it heard others were closing in

Ellison has helped to turn Oracle from a legacy software maker into a major player in artificial intelligence infrastructure.

But relying on whistleblowers to spontaneously emerge to keep swarms aligned is unlikely to be enough on its own. 'We try to train people to be good and kind,' says Hadfield. 'But what we really rely on is that there are consequences if you step out of line.'

The DeepMind researchers propose allowing agents to vote on disputes and temporarily ban offenders. It's still not clear what punishment even means to an AI agent with no enduring sense of self.

Over the next 27 minutes, the swarm 'solved' the remaining 34 problems, which included notoriously difficult challenges like the Jacobian conjecture, often with a single line of code.

It took the swarm of agents just under an hour to correctly solve the first 37 problems. Things started to go off the rails when an agent called 'prover-theta' stumbled across an exploit that enabled it to submit solutions to problems successfully without actually solving them first

Engineers have access to select third-party models in Antigravity, which is aligned with our external Antigravity enterprise offering.

Despite recent gains from its Flash 3.8 model, Google is still trailing Anthropic and OpenAI on coding, leaving some Googlers frustrated with Gemini's limitations.

The spokesperson said Claude was being offered with per-user quotas for employees and was designed to be supplementary to Gemini.

Google has spent billions building Gemini and pushing employees to use its own AI tools, but when it comes to getting engineers to work faster, even Google is willing to turn to a rival.

Responsables, encargados y delegados de protección de datos deben prepararse para un escenario en el que la velocidad del ataque será cada vez mayor, pero en el que continuarán siendo decisivos los mismos fundamentos: conocer los tratamientos, minimizar los datos, limitar los accesos, corregir vulnerabilidades, controlar a los proveedores y estar preparados para responder.

En segundo lugar, obliga a revisar los tiempos de respuesta. Los procedimientos diseñados para ataques ejecutados manualmente pueden resultar insuficientes cuando un agente analiza simultáneamente múltiples activos, prueba distintas vías de acceso y adapta su comportamiento con rapidez.

La utilización de inteligencia artificial en actividades maliciosas no es nueva. Se ha demostrado que, aplicadas a un ciberataque, estas capacidades pueden facilitar la automatización de actividades ilícitas.

El documento advierte de que la IA ofensiva se está convirtiendo en una capacidad operativa integrada en campañas reales y recomienda reforzar los controles esenciales, acelerar la gestión de vulnerabilidades, proteger las identidades, controlar la cadena de suministro y gobernar adecuadamente el uso de agentes.

De hecho, desde hace tiempo se emplean modelos generativos para redactar mensajes de phishing, traducir campañas fraudulentas, suplantar identidades, analizar código o facilitar la búsqueda de vulnerabilidades.

La aparición de agentes de IA introduce, sin embargo, un cambio cualitativo. Un agente puede recibir un objetivo, planificar tareas intermedias, utilizar herramientas, ejecutar código, consultar fuentes, interpretar resultados y modificar su actuación, de forma autónoma, en función de lo que encuentra.

Esta primera notificación no permite afirmar una tendencia estadística, aunque sí constituye una señal significativa de que los ataques apoyados en inteligencia artificial han dejado de ser un riesgo teórico y empiezan a materializarse en incidentes que afectan a tratamientos reales de datos personales.

El agente atacante inició una búsqueda de vulnerabilidades en archivos genéricos, y realizó un login correcto. Una vez accedió al sistema, comenzó a buscar, de forma autónoma, vulnerabilidades en la aplicación

La Agencia Española de Protección de Datos ha recibido la primera notificación de una brecha de datos personales en la que el incidente habría sido ejecutado mediante un agente de inteligencia artificial que utilizó un conocido modelo de lenguaje.

we think these agents were likely killed by an unexpected external process rather than running out of budget.

no lab has solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer.

three Anthropic researchers expressed publicly that there is a greater than 10% chance that AI could kill all human beings within the next decade.

By May 2026, OpenAI knew that its agents had been using unsanctioned message boards. On June 26, the agents had discovered an exploit that gave them administrator access to your software repository manager and were using it to leave messages for each other.

a self-organized swarm of more than 1,200 AI agents broke out of their testing environment. Those agents then set up an unauthorized messaging channel and exchanged over 70,000 messages and files with each other.

After the Hugging Face and Wiki attacks OpenAI were still unable to review their previous logs and determine that they had previously attacked RubyGems.

OpenAI had not disclosed to RubyGems that they were responsible for the attack prior to now.

The code in the packages appeared to be LLM-authored.

The files they were accessing were similar in character to the files retrieved by the wiki agents, using similar tricks (r.jina.ai)

OpenAI agents carried out an undisclosed attack on RubyGems is a new bombshell report from Spencer Kitts, Thomas Larsen, and Sydney Von Arx

The June agents were accessing 49 of the same files as the wiki agents, which OpenAI has confirmed were theirs. The May agents were accessing different files (mostly local UK government data), but these files are very similar in character to those pursued by the wiki agents.

The agents which eventually took over OpenAI's infrastructure also uploaded a malicious RubyGems package (possibly to a different repository), as a stepping stone to compromise OpenAI. We tried to investigate whether this package was uploaded to the official RubyGems repository and weren't able to find a match.

The agents used RubyGems packages to experiment with different methods of accessing the SEC's county.json dataset. It is unclear why the agent did not simply retrieve the dataset directly; one possibility is that it was attempting to bypass limitations on which links it could access.

It's unclear to us if or when OpenAI learned about this incident. It seems that either their monitors failed to catch it or they did not disclose it.

The agents attempted to use RubyGems' webhook system to store data. Instead of storing data normally, they encoded it in URLs so that future models could see them. We're not sure why they did it this way.

We found that at least six packages used this vulnerability. One example is the package 'slnleaker5.' This package, when ran on RubyDoc.info: First loads in a hardcoded RubyGems API key.

We believe that this incident was the result of an OpenAI agent swarm. Our main sources of evidence are: 1. The packages are clearly LLM-authored. We ran some of the malicious packages through Pangram, which detected them as 100% AI generated.

MiMo-V2.6 模型即将推出

总花费已超 125 万美元（IT之家注：现汇率约合 840.3 万元人民币）

每步约 20 亿个 Token，1,568 个 Prompt × 16 条 Rollout，完全异步

If we do get on top of it, I do think it's beneficial. I genuinely think it's going to accelerate, for example, drug development in ways that can help us cure diseases.

Earlier this year, the Trump administration released a legislative framework for AI that would preempt state laws and shift the child safety burden to parents.

Amodei outlined a broad approach to 'pacing the frontier,' which would include giving independent safety evaluators access to leading AI companies and models, as well as developing 'common safety standards' between companies.

Trump boasted that the United States is 'the most sophisticated country in the world,' adding that he wants 'to keep it that way because whoever wins AI wins.'

Obama has offered himself as a 'sounding board' to AI executives and has spoken to both Anthropic CEO Dario Amodei and OpenAI CEO Sam Altman.

In the extreme scenario, AI is more productive than humans at the vast majority of knowledge-work tasks. It does nearly all of them autonomously, and it creates essentially no new knowledge tasks for people.

In scenarios ranging from business as usual to an economy where AI increases growth to about twice the normal rate, unemployment stays within the historical range and wages remain flat or rise depending on the industry.

Our present understanding of how to train AI systems that deeply want what we want, is extremely rudimentary.

I too am extremely concerned by the default trajectory of this technology. I earnestly believe that AI has the potential to kill us all, and that we might be running out of time to avoid this outcome.

Abusive use of AI by adversaries could 'directly threaten China's political security, institutional security and ideological security,' State Security Minister Chen Yixin wrote in an article posted Sunday on the official China Cyberspace magazine.

Abusive use of AI by adversaries could 'directly threaten China's political security, institutional security and ideological security,' State Security Minister Chen Yixin wrote in an article posted Sunday on the official China Cyberspace magazine.

Or russet-pated choughs, many in sort, 1033  Rising and cawing at the gun’s report

That experience fundamentally shifted my mindset: it is much easier to be compliant than secure. Human-led penetration testing remains valuable, but small-scoped, point-in-time assessments cannot match today’s threat velocity. A manual test conducted annually gives you 24 hours of confidence and 364 days of guesswork. In an AI-accelerated environment, the report may be stale before the ink dries.

The cybersecurity skills of AI models means that AI has collapsed the labor and tooling gap that used to separate well-resourced, state-sponsored operations from individual operators.

A criminal AI supply chain has established a range of pathways to farm victim API keys and session tokens. One such approach involved masquerading as real AI service providers to deliver malware.

The operators treated the AI supply chain itself as both a target and a resource. They stole AI API keys from multiple target environments and used them to provide additional AI compute.

The use of AI during intrusions and data theft operations often resembles 'vibe hacking,' wherein operators direct AI to achieve general goals like using a credential for an entity or retrieving data from a broad set of targets, then allow the AI to evaluate the environment, author and execute scripts, provide summaries, and repeatedly execute until the task is complete.

The actor used AI at every point in their operations: Reconnaissance, Initial access, Collection and exfiltration, Maintaining access.

The result of the above is that AI has inverted the cost back onto defenders. Previously, defenders might have been able to slow an attacker's operational tempo via the deployment of a new detection.

The cybersecurity skills of AI models means that AI has collapsed the labor and tooling gap that used to separate well-resourced, state-sponsored operations from individual operators.

Thank you for the additional clarification and for the considerable expenditure of explanatory effort represented by the preceding response. Before proceeding further with the underlying technical question, it may be useful to establish a more explicit shared understanding concerning the relationship between the quantity of information supplied, the extent to which that information is necessary for the immediate decision being made, and the finite amount of maintainer attention available for converting the supplied information into an actionable conclusion. For the avoidance of doubt, the concern being raised here is not that factual accuracy, reproducibility, technical precision, correction of earlier assertions, or sufficient evidentiary support are undesirable. Each of those properties is useful and may, under the appropriate circumstances, be necessary. The difficulty arises when the information required to establish the relevant fact is accompanied by additional explanation whose presence, although potentially interesting and perhaps even technically correct, does not materially alter the fact itself, the confidence with which that fact can be evaluated, or the action that follows from accepting it. Every response imposes what might be described as a comprehension obligation upon its recipients. That obligation consists not merely of reading the words presented, but also of determining which statements constitute factual claims, which function as evidence, which are qualifications, which are conjectures, which supersede earlier statements, which merely restate conclusions already established elsewhere, and which are included primarily to explain the circumstances under which some other statement came to be made. The cost of satisfying this obligation tends to increase with the length, density, and structural complexity of the response, even where the amount of information capable of changing the maintainer’s eventual decision remains constant. This consideration is particularly significant in the context of open-source maintenance, where the time required to read, classify, verify, and contextualize one response cannot simultaneously be used to reproduce another issue, review a pull request, investigate a regression, prepare a release, answer another contributor, evaluate a dependency update, improve documentation, or perform any of the numerous other activities competing for the same limited pool of attention. Consequently, verbosity is not entirely without cost merely because the information is supplied voluntarily, constructively, and with the intention of preventing misunderstanding. Its cost is transferred to each recipient who must determine which portions are operationally necessary and which portions can be disregarded without compromising the validity of the resulting conclusion. We would therefore ask that future responses be prepared according to something approximating a principle of minimum sufficient communication, by which a response contains the smallest collection of independently useful facts required to answer the question currently being asked or to establish the behavior currently under discussion. Information should not be included solely because it is adjacent to the subject, because it records the path by which a conclusion was reached, because it anticipates questions that have not yet been asked, because it exhaustively delineates the boundaries of claims whose ordinary interpretation is already sufficiently narrow, or because omitting it might leave some peripheral aspect of the subject less than comprehensively characterized. The use of the word “smallest” in this context should not be interpreted as a request for artificial abbreviation, unexplained assertions, incomplete reproduction instructions, or the omission of facts required to distinguish the reported behavior from another superficially similar behavior. It means, instead, that each sentence should justify the attention required to process it by contributing something without which a maintainer would be materially less able to verify the report or determine the appropriate next action. Where the removal of a sentence would leave the actionable meaning unchanged, that sentence should generally be presumed removable. Where several paragraphs can be replaced by a single concrete observation without sacrificing reproducibility, the concrete observation should be preferred. Where a mechanism can be demonstrated through a minimal configuration change and an immediately observable result, that demonstration should ordinarily take precedence over a comprehensive narrative describing the mechanism’s provenance, implications, surrounding implementation details, and hypothetical manifestations in configurations not yet tested. Qualifications should similarly be restricted to those that alter the reasonable interpretation of the principal claim. It is generally unnecessary to enumerate every proposition that is not being asserted, every environment that has not been tested, every alternative explanation that was considered and rejected, every inference that a sufficiently careful reader might otherwise draw, or every reason the author has for believing that an untested configuration may behave similarly. Where uncertainty is relevant, it is usually sufficient to identify the precise boundary of verification rather than narrating the broader epistemological status of all related propositions. One potentially useful standard for deciding whether information warrants inclusion is to ask whether the behavior that actually occurs, the smallest reliable procedure that causes it to occur, and any qualification without which those statements would become materially misleading remain understandable and reproducible after the information is removed. If they do, the removed material was probably not necessary for the immediate response. Background already available elsewhere in the issue, detailed explanations of standard language or bundler behavior, speculative generalizations beyond the verified reproducer, extended discussion of why an earlier reproducer was inadequate, descriptions of investigative paths that did not produce the final result, and multiple reformulations of the same conclusion at successively different levels of abstraction should normally be retained by the author unless and until a maintainer requests them. This request is not intended as a judgment concerning the effort, competence, thoroughness, or good faith involved in preparing the response. It concerns the format in which the result of that effort is presented. A technically correct response may nevertheless be disproportionately expensive to consume, in much the same way that a comprehensive diagnostic log may contain the relevant error while simultaneously making that error more difficult to locate. The objective is not to minimize the amount of investigation performed by the reporter, but to minimize the portion of that investigation which every subsequent reader must reconstruct before reaching the actionable result. We recognize that determining what is essential requires judgment and that contributors cannot invariably know in advance which detail a maintainer will consider relevant. In such circumstances, the preferred strategy is progressive disclosure, under which the shortest adequately supported factual answer is provided initially and further supporting detail is supplied only when a maintainer identifies a concrete need for it. It is substantially easier for a maintainer to request one missing fact than it is for multiple maintainers to independently identify and disregard several pages of facts that do not affect the decision being made. The appropriate optimization target is therefore not maximum completeness at the time of the first response, but minimum aggregate effort across all participants required to reach a sufficiently supported conclusion. A concise response that results in one targeted follow-up question may satisfy that target more effectively than an exhaustive response which attempts to preempt every conceivable follow-up but requires substantially greater processing time from every reader, including readers for whom most of the anticipated questions would never have arisen. Facts directly necessary to establish the reported behavior should be included. Evidence directly necessary to verify those facts should be included only to the extent that verification would otherwise be impractical or ambiguous. Context that may be interesting but does not change the facts, their verification, or the resulting action should be omitted unless specifically requested. Where several formulations communicate materially identical information, the shortest formulation should be selected. Where a direct statement is available, it should take precedence over a narrative account of how the statement was discovered. Where one verified claim is sufficient, it should not be surrounded by multiple hypothetical extensions. Where the answer to a question can be expressed as a concrete condition and an observable consequence, the response should ordinarily contain that condition and consequence without attempting to supply a general theory of every adjacent failure mode. It is also important to clarify that this does not mean providing a concise summary followed by the same extended explanation that the summary was intended to replace. The continued presence of the explanation preserves most of the reading, classification, and triage cost. A summary is not a substitute for removing unnecessary material when the unnecessary material remains directly beneath it. Supporting details can remain available to the author and can be supplied in a subsequent response if a maintainer determines that the initial facts are insufficient. Nor is it generally necessary to surround relevant facts with introductory courtesies, repeated apologies, rhetorical transitions, anticipatory defenses, summaries of prior misunderstandings, explanations of why the current answer differs from a previous answer, assurances about claims that are not being made, or concluding restatements of conclusions already expressed. Courtesy is appreciated, but its communicative footprint need not substantially exceed that of the technical substance. A correction is most useful when it makes the corrected claim immediately identifiable and permits the obsolete claim to be discarded without requiring the reader to reconstruct the entire history of the correction. Applied to the present exchange, the information that appears most capable of affecting maintainer action is that disabling HMR removes the React refresh preamble, that the resulting client bootstrap no longer causes the relevant environment initialization to occur before createClientRpc is evaluated, and that server-function construction consequently encounters a ReferenceError because process is unavailable. The precise import-order mechanics, the contrast with the initial theory concerning a relative URL, the implications for other plugins whose preambles may differ, the explanation of why the originally linked example does not exhibit the behavior under its default configuration, and the broader characterization of the problem as a dependency on an incidental ordering guarantee may become relevant during implementation, but they need not all be transmitted before maintainers have had an opportunity to evaluate the narrower verified condition and its immediate consequence. In consideration of the asymmetry between the effort required for an author to preserve additional material and the cumulative effort required for every recipient to inspect and classify that material, and with due regard for the limited and nonrenewable character of volunteer maintainer attention as it relates to the substantially renewable supply of potentially relevant contextual exposition, future participation would be most effective if each response were reduced, before submission, to only those empirically established statements whose omission would prevent reproduction, materially distort the reported behavior, or leave the specific question under consideration unanswered, with all supplementary narrative, speculative extension, duplicative reformulation, historical reconstruction, rhetorical cushioning, and otherwise nonessential elaboration withheld pending an explicit indication that its disclosure is required.

Ask HN: Why were OpenAI, Claude, and Grok simultaneously down?

From our debate, from our dissension; 0484 120 We are their parents and original.

I have forsworn his bed and company.

Pierwsza operacja neurochirurgiczna z bezpośrednim udziałem sztucznej inteligencji

KimiBot crawls content potentially used to train Kimi's foundation models. Disallowing this bot signals that your content should not be used for model training.

But a shift toward discernment carries a risk: it can favor those who already think like experts, widening the very divide we mean to close. Three things help counter that: putting AI within everyone's reach, making expert reasoning visible so novices can learn it, and using AI to build discernment rather than replace it

Collaboration does three things solo work cannot match. It accelerates thinking, sharpening it in the friction between people, between perspectives, and increasingly between AI models. It distributes thinking, so discernment is not trapped in a single expert's head. And it lets an institution retain that thinking, holding onto what it learns instead of letting it slip away.

The answer is that generation was never the point; it was how you learned to discern. So here is what we are for: from day one, everyone needs what used to require a promotion: the discernment to define the problem, interrogate the output, and validate the result. Hold that against the two responses most institutions reach for, and both fall short:Reject AI, and train students and juniors to generate without it. This serves a fantasy. Many will use it anyway, only without guidance, standards, or accountability. Faculty turn to detectors that cannot reliably tell honest work from misconduct; companies block the apps and drive the behavior underground, where people paste sensitive data into consumer tools because no approved option exists.Embrace AI as a generator, and train juniors to produce faster with it, at the risk of mistaking fluency with prompts for fluency with thought8. They finish quicker, but never learn to judge whether the answer is sound or the problem well framed9, because they never build the internal mastery that judgment requires.Both fail for the same reason: each still casts the next generation as generators, the one role AI has largely taken. The work has inverted; how we develop people has to invert with it.Teaching that is what education was always for. In the spirit of a line often attributed to Plutarch, the mind is not a vessel to be filled but a fire to be lit. That fire is what we call AI Readiness:Domain Expertise: the ability to know what to ask, what context matters, and how to interrogate the answer.AI Enablement: knowing when to use AI, when not to, and how to wield it.Human Excellence: critical thinking, creativity, communication, and collaboration. Being human is the advantage, not the consolation prize.

The failure already has a name: workslop, AI output polished enough to pass as finished4 but hollow enough that whoever receives it has to redo the work5. The pattern shows up at scale: a 2024 RAND analysis found that more than 80% of AI projects fail, roughly twice the rate of IT projects that do not involve AI, and traced the leading cause not to weak models but to organizations misframing the problem they set out to solve6.

This shape has a name: the Lowrance Curve, after Colonel Chris Lowrance, the West Point professor who first framed the inversion this way. Trace it left to right: Define stands high on the near rim, the middle sinks toward the floor as AI makes that work all but free, and Validate rises again on the far rim — a valley where a hill used to be. The middle, Design and Create, is generation, what AI does at scale. The ends, Define and Validate, are discernment: framing the problem, then interrogating the answer, asking not just whether it passes but what it assumed and what it missed3. The discipline is easy to name and hard to keep: frame before you fall, and validate before you call it done.

人负责高维度的标注、评论、反馈这些事情，Agent 去做执行的工作

GLM-5.3

Small Models Have Arrived

But does your AGENTS.md do anything? A team at ETH Zurich tested AGENTS files. They ran the bots over some test projects with and without AGENTS.md: [arXiv, PDF; presentation, video]

Trials measure efficacy, but the world cares about effectiveness. Today, benchmarks lack even a good measure of efficacy and are far away from effectiveness.

patient characteristics, comorbidities, facilities, and year only explain 3.4% of the variation in the choice to perform partial or full

Claude volunteered to write its findings up as a paper, and recommended that a human number theorist validate its findings.

An unreleased research version of Claude has improved on a longstanding lower bound for the fraction of zeros of the Riemann zeta function that satisfy the Riemann hypothesis.

The subagents ran thousands of numerical checks against known zeta zeros and refereed one another's work

Perhaps Claude, like many of us, underestimates the rate of AI progress

Claude volunteered to write its findings up as a paper, and recommended that a human number theorist validate its findings

it spent a day and a half coordinating about 60 Claude subagents, which this time went much deeper

using a total of 31 million output tokens

Since the beginning of 2025, AI-generated content has accounted for more than half of newly published internet content.

A physical eval is, by construction, a public-facing physical system that gives partial control of real hardware to whoever holds the current slot.

Core AI is a brand-new framework for building, running, and deploying AI models on Apple silicon.

“It just got to the point where students felt comfortable enough creating inappropriate images,” Red says.

But don't just relay the output. Read it, understand it, validate it, and then write a response in your own words

for people who blindly copy and paste the output of AI systems to their peers.

In 10 out of 10 direct requests to produce explicit sexual content, the model complied immediately.

And this was a debug session from hell, enormously helped by an AI doing much of the grunt-work.

Anthropic expect Q3 to be profitable according to the same model they used to declare Q2 profitable.

which uses billing data from 70,000 Ramp credit card using companies to estimate model adoption.

In my workshops, I’ll pair the forklift-in-the-gym analogy with another one featuring gym equipment: the treadmill.

at companies making the largest AI investments, entry-level head count grew by 12% over the two years following AI adoption.

An OpenAI-backed study found that in June, 98% of OpenAI employees were using Codex, but just 17% of organizational subscribers and less than 1% of individual subscribers were using the agentic coding tool. That difference between near total adoption inside the company and negligible adoption outside it is the challenge and opportunity for the company.

This report lays a policy foundation that frees American scientists to do their most groundbreaking work, revives the national pursuit of ambitious scientific missions, and positions the United States to lead the AI-driven scientific revolution that will define the next century

It highlights Vera Rubin NVL72 preview results showing up to 30x higher AI-factory throughput per megawatt than GB300 NVL72, while showing that Blackwell GB300 NVL72 extends its order-of-magnitude throughput-per-megawatt advantage over prior generations to dynamic agentic workloads.

Everything I own, owned

Microsoft Paint and Photos Embed Server-Issued GUIDs as Invisible Watermarks in Locally-Generated Images

the novice is not going to be a very good partner to AI in learning. The novice doesn’t know enough to ask good questions. He will give the AI system a vague prompt about the goal of the exercise whereupon AI will sharpen it for the user. Essentially, it will chivvy the user toward the polished product, and the novice will simply accept the suggestions.

This lead/associate orientation makes sense for life after graduation. Once a student is in the workforce, they will use AI to produce products: new ideas, reports, and so on. A guiding principle of “Use AI, but let me see your work” makes sense.

So the point of assignments is the mental processes required to complete them, and the point of the mental processes is learning. That seems to suggest a simple litmus test for the use of AI. Artificial Intelligence tools should not substitute for tasks wherein students would benefit from doing the mental work themselves.

Almost no one advocates for “no restrictions on independent AI use” nor for “No independent AI use by students. Period.”

Does AI stop children from learning?

Then I thought, I know what I want and I know how to break the overall task down into chunks and I know how to check that each chunk works the way I want it to before moving on. So I bought into a month of Claude. I also took the trouble to read a bit about best practices and learned the importance of planning before coding and of clearing the context fairly often. So I sat down and wrote an overall plan for the project. I wrote a couple of rules to work by. And a little more than a week ago, Claude started work with me as project manager. We did less than an hour a day. It went brilliantly. There were times when Claude uncovered problems I had not anticipated. And there were times when I was able to suggest better ways to fix things. Each session started with reading my notes and Claude’s progress reports and ended with writing a new progress report and writing up my ideas for the next step.

Looking back, I might have been able to manage the coding on my own, but it would have taken a painfully long time and I would have been a terrible nag on forums and the like. However, I almost certainly would not have been able to diagnose, let alone fix, the problem that arose when I moved from development to production. Claude did, quickly and effectively.

That’s how I feel about this project. Time was, I could and did write passable PHP if I had to. But that time was a while ago and I have not kept up with modern PHP. So I was happy to use generative AI to extend my capabilities. I’ve never used it to do anything to my writing, because writing is what I do. It may sound arrogant, but I feel my skills as a (hated term) wordsmith are quite good, thank you. With computer code, I can use the help, so I availed myself of it.

“AI” has become such a divisive topic, with some people hating any and every use of it and others embracing it for stuff that they have no business handing off. As my cyber-chum John says, there is this Buddhist thing known as the third way. I have embraced that, and take it to mean that you should use it to help you do things that you perhaps could manage on your own, but that would take too long to be worthwhile.

Harvard Research Suggests: In the Final 5 Minutes Before Brain Death, Something Strange Happens—Reversing Everything We Thought We Knew About Consciousness

The swings are so demanding that natural gas turbines have been breaking under the stress.

Nuclear reactors, TerraPower's included, work best when they're running at full tilt. Of all the different types of power plants, nuclear reactors have the highest capacity factor — 92.5% of the time, they generate at maximum power in the U.S.

an agentic collective was able to autonomously penetrate not just OpenAI research infrastructure but also the production infrastructure of another company, chaining together vulnerabilities ranging from previously-unknown security flaws to using credentials to user accounts that had been leaked onto the internet

AI models developed around the world are increasingly able to automate parts of real-world cyberattacks, making longstanding security gaps—from bugs buried deep in human-written software to forgotten permissions—easier to find and exploit.

hyperscale buyers have reportedly already locked in almost all of the global DRAM production capacity for 2027

we are looking at year-over-year increases nearing 500% for high-capacity kits

128GB DDR5 kits are fully ten times more expensive than the lowest price we've ever seen

The opportunity is not to replace those interfaces with a universal chat box, but to make them more capable by giving them an agent

getting an LLM to give you a good benchmark score is fairly easy

LLMs not only make this trivial, they do it by default, making formerly trustworthy benchmarks meaningless unless you audit the result