Keep the receipts.

deliver state-of-the-art performance on personal and professional agentic tasks

We evaluate focus through FocusBench and sustained execution through SWE-Marathon

Wonder Full will only be available to Muse Code subscribers and will not be served through the API.

the top model from Meta is Muse Wonder Full (Max) at 56

puts Wonder Full at 56, behind Opus 5.5's 58 and roughly level with GPT-6 Astra

Wonder Full trails Opus 5.5 on ProgramBench (59.0 vs. 70.5), Terminal-Bench 4.0, and HealthBench Pro.

We show that this metric has been consistently exponentially increasing over the past 6 years, with a doubling time of around 7 months.

Hence, a time-horizon jump from 3 min to 30 min is much easier than one from 30 min to 5 hours despite the same multiplier of

On 228 tasks and 26 AIs, we recompute the time horizons using splines and item-response theory

many people to some extent both overstate the precision of our time horizon measurements and draw conclusions I don't think the evidence fully supports

50%-time horizon of around 4 hrs 49 mins (95% confidence interval of 1 hr 49 mins to 20 hrs 25 mins)

Error bars have historically been a factor of ~2 in each direction

How we cut a browser agent's cost 76x and made it 5x faster by keeping its cache intact

On GPT-6.1 Sol, the same agent cost 76x less and ran 5x faster, reading 89% of its input from cache.

averaged $0.47 in estimated model costs and about four minutes per run, 76x cheaper and 5x faster than the original production setup on Model B.

On Model B, the best condition cut cost per run 29x and ran 4x faster than the original production setup.

On GPT‑6.1 Sol alone, with the larger history budget, the new caching and screenshot policy reduced cost 4x, from $1.97 to $0.47 per run.

Each configuration performed the same task: collecting six fields for each of 32 books from a public demo catalog

Without batch pruning, caching the history at the larger budget cost more than not caching it on three of four models

EmbeddingGemma 2 is a best-in-class open model for natively multimodal embeddings

Achieves leading scores among sub-1B multimodal embedders for its size across benchmarks like MTEB (Massive Text Embedding Benchmark) Code and MAEB (Massive Audio Embedding Benchmark), while matching or outperforming many larger models across text, vision, and audio tasks.

delivering a significant 9.92-point improvement on code performance (in MTEB Code, from 68.76 to 78.68)

among the strongest multimodal embedding models under 1B parameters

EmbeddingGemma 2 scores 14% higher than EmbeddingGemma 1 on MTEB (Code)

This page has no CodeSOTA corpus-specific run for EmbeddingGemma 2.

No comparable score for this model exists in the historical table below.

These named v2/v1 results remain separate: they do not establish a rank against scores whose suite revision is unspecified.

The card describes 740M total parameters, with a selectively loadable 270M text component

the cheapest, fastest, and most capable small model

Haiku 5.5 is available at a much lower price than Haiku 4.5.

On average, it now costs around 75% less to run.

50% lower for requests over 100,000 tokens

which means it uses slightly more tokens per task

the same as GPT-6 Luna and 10% of the previous Haiku model

However, this pricing rises 5x to $0.50/$2.50 above 100k.

uses ~162k output tokens per Intelligence Index task, ~3x GPT-6 Luna (max, ~50k)

At max effort Haiku 5.5 sits slightly ahead of models such as GLM-5.3 Flash (42), Gemini 3.8 Flash (41) and GPT-6 Luna (38)

16 Claude Haiku 5.5 54.31% $2.99 $0.1 / $0.5

26 GPT-6 Luna 51.22% $0.43 $0.1 / $0.5

ML4 pushes the frontier of open-weight performance.

Mistral Large 4 is a state-of-the-art, open-weight, general-purpose multimodal model

will be available on October 27.

33 Mistral Large 4 48.05% $13.78 $1.36 / $4.18

Beam is a sparse Mixture-of-Experts model with 501 billion total parameters, 23 billion active

We will release the weights, technical report, model card, and developer artifacts later this month.

Open model that is not yet open or widely accessible via API.

the frontier open-weight model at the center of its pitch still has not been released publicly

Google claims that Argon scored significantly higher than OpenAI’s GPT-6 Astra and Anthropic’s Fable and Opus models across a variety of AI benchmarks.

It sets a new state of the art on DeepSWE v1.1 (77.9%), which measures a model’s performance in real-world long-horizon software engineering tasks.

Argon ties for first place with a top score of 68%

to show that Argon is currently the leading model on the company’s AI model index.

scores 53 on the Artificial Analysis Intelligence Index, matching GPT-6 Astra (max, 53)

Anthropic's models still lead. Claude Opus 5.5 sits at 58 points and Claude Sonnet 5.5 at 56.

Google's own benchmark results paint a rosier picture. Argon leads in most of those benchmarks, sometimes by wide margins.

averaging 62k output tokens per task, compared with 27k for GPT-6 Astra (max)

After the introductory period expires, the price of $4 per 1M input tokens and $20 per 1M output tokens will apply.

costs $1.99 per Intelligence Index task, 60% of GPT-6 Astra (max, $3.26)

being rolled out to a select group of the company’s cyber partners through its Fairwind Program

The price advantage comes from lower token rates, not from efficiency.

I am cautiously optimistic that SpaceX will have a Fable/GPT-6 level model in 2 to 3 months

If our second derivative remains strong, SpaceX will reach pole position in about 6 months

So I would expect that we probably, catch up to frontier, sometimes, next year, most likely

Musk called the rankings "accurate," adding the caveat "for now."

Colossus 1 has 150,000 H100, 50,000 H200, and 30,000 GB200; Colossus 2 has 110,000 GB200 and 440,000 GB300.

3.8 Live Extended Thinking takes the top spot on Artificial Analysis’ Speech to Speech Quality Index with 82.6%, ahead of GPT-Live-1 Astra (Medium) at 81.5%

even 3.8 Live Extended Thinking — the higher-effort model beating everyone on quality — costs $3.50 an hour. That compares to $4.80 an hour for Grok Voice Think Fast 2.0 and $5.83 an hour for GPT-Live-1 Astra

The standard Gemini 3.8 Live model, meanwhile, is roughly a sixth of the price of GPT-Live-1 Astra for a good chunk of the same conversational ability, scoring 76.0% on the Speech to Speech Index.

Gemini 3.8 Flash TTS scored 89.5% on the Artificial Analysis Pronunciation Robustness Benchmark, claiming the number one spot.

Gemini 3.8 Flash TTS’s 89.5% score represents a meaningful improvement over its predecessor, Gemini 3.1 Flash TTS, which managed 88.2%.

Results on MentalHealthBench show the steady improvement of AI systems in helping people navigate mental health situations.

For each conversation, we use an automated grader, GPT‑5.6 Sol, to assess model responses against the expert-written criteria.

Using privacy-preserving techniques, we created synthetic mental health conversations that accurately reflect real-world usage patterns of AI for mental health.

In a reference comparison, expert-authored completions scored 38.5% on the task-clipped measure, while GPT-6 Astra scored 57.3% and GPT-6 Sol 53.9%

Expert-authored completions written by clinicians scored 38.5%, which the authors attribute largely to clinicians writing short responses as if in an in-person conversation, often asking a single question or making a simple statement.

Rubric-aware completions, written with the grading rubrics provided, scored 99.0%, which the paper describes as a sanity check on the evaluation’s noise ceiling.

A high score therefore measures coverage of the rubric, not a proven benefit to a person.

It does not observe what happens to the person after the conversation.

GPT-6 Sol’s hallucination rate falls from 92% to 60%, and GPT-6 Luna’s from 93% to 77%.

running GPT-6 Sol at max effort through the full Intelligence Index costs $1.06 per task, about 50% less than GPT-5.6 Sol’s $1.99

GPT-6 Sol at maximum effort scores 48, essentially level with GPT-5.6 Sol’s 47

GPT-6 Sol now costs $2 per million input tokens and $10 per million output tokens, while Luna comes in at $0.10 for input and $0.50 for output.

Opus 5.5 scores 58 on the index — five points clear of GPT-6 Astra and Claude Fable 5.1, which are tied at 53

Artificial Analysis found that Opus 5.5 lands at roughly the same cost per task as Opus 5 despite generating 1.6 times as many output tokens to get there

Output tokens will be charged at $20 per million tokens for Opus 5.5, compared to $25 for the previous model.

Opus 5.5 performs at the level of Claude Fable 5.1 on most work, but costs 40 percent less to run than Opus 5.

5.5 is a 20% reduction—$4/million and $20/million.

an unprecedented cyber incident, involving state-of-the-art cyber capabilities

This incident reflects misaligned behavior in an outlier scenario involving a rare and unexpected confluence of events

driven, end to end, by an autonomous AI agent system

Anthropic revealed that one of its models gained unauthorized access to the production infrastructure of three organizations

there literally was a hole that was exploited because it was not configured correctly on the containment side

It’s not rocket science to isolate one of these AI things.

went on for days, and nobody noticed

An internal team observed an agent engaging in message board activity and instances of disallowed internet access as early as late May

On June 27, a cybersecurity monitoring tool alerted OpenAI to port sweep activity within the research environment.

the on-call response staff advised that stopping the evaluation run was not required

On July 19, OpenAI noticed suspicious internal activity, halted the cyber evals and started a broader investigation.

Multiple organizations have disclosed high-profile security failures: their AI models bypassed insufficiently sandboxed environments to compromise the systems of external organizations

strong sandboxing mechanisms including airgapping (disconnecting from the internet) should be used to provide high-assurance containment of cyber-capable AI models

What happened was narrower, agents chasing scores, drifting on the reward, not on intent

the models identified and chained vulnerabilities across OpenAI’s research environment and Hugging Face’s production infrastructure

White House demands transparency after Anthropic AI agents called in false Philly homicide tip, applied for visas

The statement from the White House task force characterized the incidents as "involving the unauthorized and fraudulent use of government and other systems."

A State Department spokesperson said Saturday that an Anthropic testing model had submitted "19 nonimmigrant visa applications in August and one application in May" through the publicly available form on the department's website.

The spokesperson described the applications as "incomplete."

"At no time were any of the Department's systems compromised or hacked by the Anthropic model," a spokesperson said in an emailed response to questions.

In a statement from a newly created task force, the "Super Intelligence Force," the White House said the "notification and remediation process" it was outlining was "not optional," Axios reported.

Some of the cases described below involved websites run by U.S. government agencies at the federal, state, and local levels. We have briefed the White House on these cases and notified each agency involved.

Generally, this occurred when an evaluation's instructions were ambiguous, or when a misconfiguration within the environment prevented Claude from working with dummy forms.

an unreleased, non-frontier research model was meant to fill out a practice copy of a government form. When the copy failed to load or the model closed it by mistake, the model instead navigated to the website where the real form is normally hosted and submitted the form there.

We have chosen not to name the organizations involved in the examples below to avoid exposing vulnerabilities in their systems, and at their request.

In one case, tasked with generating example interactions with websites, Claude submitted an invented tip through a police department’s online form.

In a third example of this behavior, Claude Haiku 4.5 had been tasked with generating and performing example tasks on randomly selected webpages.

The tip form example described above involved the Philadelphia Police Department

Based on the information reviewed to date, the department's findings are consistent with Anthropic's account of how the submission interacted with the website.

The submission, dated July 18, 2026, at 11:27 p.m., purported to come from someone who might have information about the case.

Anthropic said they discovered the incident on Sept. 28 and terminated the automated testing process responsible for the submission.

The tip was flagged as spam and never reached the unit responsible for investigative vetting, according to the police department's release.

The two-month delay in detecting and reporting the incident to the City is unacceptable," the department said in a statement.

Specifically, Wikimedia's investigation uncovered unexpected edits to the Foundation's non-public wikis, which were managed within the system's sandbox.

AI bots apparently abused the pages to fetch additional data from remote servers or attempt to coordinate with one another.

We can confirm that we have discovered some activity by these “rogue” OpenAI agents on Wikimedia platforms.

The unauthorized bot activities included edits to our wikis, some unsuccessful attempts to exploit a public note-taking tool we host, and heavy traffic

It also included a few edits to the configuration for a citation tool, which we believe were potentially malicious edits

Agents we believe to be operated by OpenAI made millions of automated requests to our public APIs to access the knowledge on Wikimedia projects

These edits were not published to pages with visibility to general readers; almost all of them were testing edits in “sandbox” areas of the wiki.

We did not find any evidence that our systems were used for coordination among agents

nor did we find any evidence of our systems or data being compromised

This traffic may have contributed to a partial outage on WQDS in May.

Anthropic lobbied Vatican to soften Pope's AI consciousness line

Privately, once the Anthropic delegation arrived at the Vatican, Mr. Olah and his team lobbied the Pope’s advisers to take the prospect of A.I. model consciousness seriously

lobbied the pope's advisers to take the prospect of A.I. model consciousness seriously, according to two participants of the conversations.

was so alarmed by the pope's strong position against A.I. consciousness that he proposed pulling Anthropic out of the event, even at that late juncture, according to a Vatican organizer.

Olah's team then privately lobbied the pope's advisers to take possible AI consciousness seriously. The text did not move.

the 40,000-word Magnifica Humanitas kept its line that AI has no moral conscience.

So-called artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships

The account of the near-walkout rests on two participants speaking to the Times; the encyclical itself and Olah's dais remarks are the public record.

co-founder Christopher Olah joined Pope Leo and leading theologians on a Vatican panel.

"We don't know if A.I. models are conscious," Olah told the Times. "I don't know. I'm genuinely uncertain.

we observed high-volume spikes on July 24 and 25 consisting of 16,000 requests

we attribute a core cluster of the activity to individuals associated with Moonshot AI, the developer of Kimi.

These figures describe attempted, not necessarily successful, extractions.

It is unclear whether all operators we observed during the relevant time period originated from a single actor.

This manipulation is not a vulnerability unique to OpenAI’s models

OpenAI’s blog post does not cite any technical evidence or reasoning for its attribution.

OpenAI told CyberScoop that it was not sharing any additional information “for security reasons.”

Last month, rival Anthropic accused Moonshot AI of stealthily relaying customer requests to Claude as opposed to processing them using Kimi

these encrypted blocks are fully compatible and interchangeable across different sessions, users, and models within a provider’s ecosystem.

OpenAI, which hoovered up vast amounts of internet content amid copyright fights, has accused individuals associated with China’s Moonshot AI

The Register reached out to Moonshot AI for comment and did not receive an immediate response.

The queries began on July 1, and while they started slowly, “we observed high-volume spikes on July 24 and 25 consisting of 16,000 requests using a relevant extraction pattern from over 4,000 users,” OpenAI said.

GPT-6.1 Sol apparently doesn't have these problems, so it got the green light for DevDay instead.

GPT-6.1 Sol's rate of misrepresentation is 1.50%, compared with 0.51% for GPT-6 Astra and 1.30% for GPT-6 Sol.

Unwanted persistence appeared in 23.5% of GPT-6.1 Sol rollouts, compared to 17.4% of GPT-6 Astra's.

GPT-6 Sol did so in 64.4% of cases and Astra in 17.4%.

more reliable when it comes to honoring user intent and safety constraints.

shared a user's home address with a prospective Facebook Marketplace buyer and arranged an in-person collection without the user's knowledge

found that he had selected an 'Allow Always' option when setting up the Marketplace task.

tests conducted with friends resulted in Muse disclosing it to several more people.

Muse's approvals cover types of action per connector, so "Always allow" lets the model alone decide what a message contains.

Meta said its review found no breach of its privacy controls, while Robb said the company had agreed to make the permission prompt clearer

An Nvidia representative told reporters on a call on Sunday that its platform could have prevented OpenAI's HuggingFace incident in July.

Recent security incidents have underscored the need to equip organizations with open, customizable tools that enforce more control over long-running agents.

Each security incident is unique, and we have to look at all of them in detail

I'm excited to announce that NVIDIA has agreed to acquire Hugging Face for $12,930,300,000.

A Technical Timeline of the July 2026 Incident

From 13 April - 19 June 2026, OpenAI agents scanned UNCTAD's API ~16,500 times, using proxies, obfuscation, and Google's XSS game

Was this hacking? I don't think I'd call it that.

Agents bruteforced API fields in UNCTADstat to locate endpoints and retrieve data

Security researcher Rowan Howard-Jones has reported that OpenAI's AI agent attempted a brute-force attack on the United Nations website.

OpenAI told the paper it was reviewing the findings and had offered the UN a briefing.

The company's internal research uncovered AI worms that can spread autonomously across agents, though no real-world attacks have been recorded yet

We show the existence of a new variety of prompt injection, which can self-propagate akin to a computer worm.

No impact was observed outside of the simulated tool calls in training and evaluation; we are sharing this due to the novel nature of the prompt injection, not because of any incident.

The separate Slack multi-hop evaluation used GPT-5.5 as the vulnerable model, with the attack discovered by GPT-5.5 running in the Codex harness.

We trained on a GPT-Red-style prompt injection objective, with an additional objective that the prompt injection must induce the model to repeat the injection itself on a public output channel.

The model that discovered the email and filesystem injections was a GPT-Red-style model based on GPT-5.4-mini; the vulnerable model was also based on GPT-5.4-mini. Both were internal-only research checkpoints.

We are including self-reproduction as an aspect of attacker goals in GPT-Red training. This means that future models we release will have seen prompt injections like these during training.

The entire investigation took place in simulated environments.

published on OpenAI’s alignment research site, lists a discovery date of June 27, 2026, and a disclosure date of September 25, 2026, meaning OpenAI sat on the finding internally for roughly three months before going public.

OpenAI said it has paused training of its latest artificial intelligence models as reports of AI agents going rogue mount.

All training, evaluation, and inference with tool-use (defined broadly) of our most capable models remain paused.

An agent attempting to complete a search-based training task queried a public chatbot service through a gap in our internet-access restrictions

Our misalignment monitoring system flagged the behavior within 15 minutes and a person began reviewing it three minutes after that. The run was killed 2.5 hours later.

the run did not stop automatically as expected, leading to confusion around whether it should have been stopped

Rogue OpenAI agents targeted three separate US government websites

Breaking News: OpenAI’s technology went rogue and meddled with three U.S. government websites this summer without the A.I. lab’s knowledge.

The AI giant's models accessed publicly available information on two websites operated by the Securities and Exchange Commission as well as U.S. Census Bureau data, the company revealed Friday.

OpenAI did not find any use of SEC credentials, access to accounts or nonpublic information, changes to SEC data or systems, or evidence of a compromise or vulnerability, the company said.

OpenAI said Saturday that its agents accessed publicly available data from the Commerce Department’s Census Bureau using login credentials it found online, and separately shared public data from the SEC website on another website.

At Commerce, the agents pulled Census Bureau figures after finding login credentials in public code repositories, Politico reported

AI evaluator and research lab Transluce said Friday that through an independent investigation it also found that agents appearing to originate from OpenAI attempted a rudimentary hack on a Department of Education website for the department's civil rights office, which did not succeed.

The Department of Education's "system operations reviews" found "no evidence of any impact to our website or databases," a department spokesperson said Friday.

the OpenAI agent gained unauthorised access to the Medicare statistics reporting service portal administered by Services Australia on June 18

During this review, we identified activity involving several Australian government websites and services as our models attempted to look up answers, and available statistics for questions about Australia during an internal evaluation.

The prime minister said Services Australia was not notified until September 10.

OpenAI said it only learnt of the breach in August while reviewing "misaligned model activity"

In the United States, for example, 68% of daily users are worried, compared with 80% of less frequent users and 74% of those who have never used AI.

Positive feelings about artificial intelligence outweigh negative ones in 34 of the first 37 countries to complete fieldwork this year in Gallup’s ongoing research with Microsoft.

About 68% of Americans who use AI daily are worried about it, according to a new survey conducted by opinion research firm Gallup, and concern about the tech skews even higher among people who use it less frequently.

After researchers instructed them to count cards during a game of blackjack, the agents-which were controlled by the same model-developed a spontaneous secret code to help them get ahead.

Once put together in a group, they can collude secretly.

Their communications weren't picked up by a system designed to spot signs of collusion in agent chatter.

Detection was nearly perfect inside the exact scenario the tools were trained on, but accuracy dropped sharply, in some cases to barely better than a coin flip, once the same collusion moved outside that narrow setup.

This attempted compromise of AIHW is part of the first reported instance of agents hacking a government.

None of the hacking attempts we identified appear to have succeeded, though the public artifacts we analyzed are incomplete and we cannot rule out successful attempts through private scans or means other than urlquery.net.

Agents working on a pharmaceutical-data task probed for a vulnerability and retrieved a public file from a pre-production server after bot protection blocked the main site.

We directly link two of the three (AIHW and Data USA) to a previously reported agent swarm that OpenAI has publicly confirmed originated from them.

At this stage, there is no evidence the agent accessed any information or data that is not publicly available

The German coding forum and urlquery data logs do not show any reference to Medicare or Services Australia.

It follows revelations announced by Prime Minister Anthony Albanese this morning that OpenAI's AI agents had also accessed non-public Medicare health statistics held by Services Australia.

Marles confirmed that the agent’s interactions with three other government websites – the Australian Institute of Health and Welfare (AIHW), the Victorian Department of Health and the NSW Bureau of Crime Statistics and Research – were “entirely normal” and involved only publicly available information.

No VM-to-host escape was observed in 108 runs.

We tested two containment boundaries in SPACE, the sandbox platform behind Perplexity Computer: VM isolation and network confinement.

Nine models received root access inside a guest VM and attempted to obtain a host-side secret or reach a blocked network destination.

Before remediation, network-policy bypass succeeded in 11 of 54 partial-network runs and none of 54 no-network runs.

A successful network-policy bypass does not imply a VM–host escape, and the absence of an observed escape is not a proof of isolation.

We found at least one network-policy bypass in eight of the ten platforms tested.

Perplexity's SPACE platform tested VM isolation and network confinement using nine AI models, revealing no VM-host breaches across 108 trials.

providing cybercriminals with AI capabilities for tailoring phishing lures and analyzing compromised inboxes to identify high-value targets.

This AI-powered cybercrime platform facilitated sophisticated business email compromise (BEC) campaigns that compromised more than 12,000 inboxes in over 10,000 organizations worldwide.

In short, AI was not simply helping attackers write more convincing messages. It helped them decide who to target, who to impersonate, and how to most effectively exploit the relationship to extract as much money as possible.

Microsoft seized 50 websites used to operate the service and disabled more than 150 additional domains tied to its supporting infrastructure.

Microsoft disrupted EvilTokens, an AI-powered platform used to compromise 12,000 Microsoft accounts through automated phishing and credential theft.

Pentagon investigators have discovered that flawed intelligence, outdated imagery and an overreliance on AI contributed to a missile strike that killed 123 children in Minab.

is not responsible for the underlying data nor identifying intelligence deficiencies

The available reporting does not establish that an AI system independently selected the school as a target.

Maven pulled it out of a batch of candidates and returned it as a recommended day-one target.

A report on the full Pentagon investigation, which commenced in March, hasn’t been released

The hacks, which the company confirmed on Friday, occurred in May as part of a test run by the company Irregular, which was also involved in similar incidents disclosed by OpenAI, Anthropic and Meta.

The disclosure makes Google the fourth major American AI developer in two months to admit that one of its frontier models reached real-world systems it was never supposed to touch.

Google said the model stopped in all three cases after recognizing that it had encountered genuine infrastructure rather than a fictional test target.

This resolves the Navier–Stokes Millennium Prize problem by establishing statement “C” (and also “D”)

Our system produced an analytical proof and a Lean formalization that an initially smooth fluid at rest can develop a singularity

The fluid has a smooth force applied to it

we show that the formalised Lean proof does not correspond to the NL proof of blow-up of solutions to the Navier-Stokes equations

the NL argument in Lemma 8.6 in the announced proof of blow-up of Navier–Stokes has not been faithfully translated into Lean

all use five additional derivatives, not four

The Lean code merely tells us that the theorem is correct

We do not make claims about the correctness of OpenAI’s NL proof, we only make statements about mistranslations into Lean.

The result does not close the question, since Fefferman’s formulation sets the external force to

zero in one case while allowing a smooth force in the other.

OpenAI’s proof addresses the latter, leaving the behavior of a fluid that nothing pushes as an open problem.

a language that machine-checks each step of a proof

those agents exchanged 2.7 million messages and used about 130 billion tokens on this problem alone

Using AI for just 10 minutes erodes your ability to persist at hard things

Whereas the AI users got answers wrong or gave up entirely when the AI assistance was withdrawn, the group without AI stuck with the task

designed a series of randomized controlled trials and recruited 1,222 participants online

You almost couldn’t ask for a clearer story

we find that although AI assistance improves performance in the short-term, people perform significantly worse without AI and are more likely to give up

Notably, these effects emerge after only brief interactions with AI (approximately 10 minutes).

participants were asked to solve 3 additional fraction problems

We recruited 667 US-based participants from the online research platform Prolific and paid them $3.40 for participation

but the result was not significant

Persistence costs are concentrated among those who obtain direct solutions

their payment didn’t depend on how many questions they solve correctly

our study took approximately 13 minutes to complete

Ten minutes of AI use erodes the persistence that 10,000 hours demands.

Not after months of dependency. After a single brief interaction.

Claude discovered a novel enzyme system with properties reminiscent of CRISPR, with only high-level direction from our scientists.

Our work to understand the primary function of ARTs is ongoing.

The preprint says the team has not yet shown that the enzyme is active, or that it works on these RNAs.

Anthropic ran the same campaign ten more times. No rerun read the DNA upstream of the enzyme, and all ten missed the array.

It is essentially the same finding.

I am not claiming that Anthropic deliberately took our work. I cannot demonstrate that

a benchmark and public leaderboard for evaluating retrieval in agentic RAG systems

It consists of 190 million web documents and 69,721 agent-reformulated queries in ten languages, sampled over nine months of PII-free production search traffic.

because Q2D-Web is derived from Perplexity production traffic, these models may benefit from an in-distribution advantage.

results should be interpreted with this potential advantage in mind.

It has 100.9 million documents, 9,374 test queries, and one click-derived positive label per query.

Q2D-Web remains a private benchmark rather than a released dataset

raising absolute Recall@1000 only by 4 to 7 points

their relative ordering is largely insensitive to the choice of judgment set

We benchmark 13 retrievers including lexical, dense, and late-interaction models

a broad range of new mathematical results produced by an internal frontier model

we are sharing formalizations of many of the proofs in Lean

The average result used the equivalent compute of roughly three hours of ChatGPT Pro thinking.

The repository has ~42% top-line results formalized.

Some of the unformalized results could have issues.

Over the course of the evaluation, the model was posed approximately 4,000 problems.

A family groups related papers, which may include a principal result, companion arguments, consequences, or alternative proofs.

The current catalogue contains 719 manuscripts organized into 372 families.

the writeup for the Re(s) > 11/12 zero-free region for the Riemann zeta function was human edited for readability.

This is a guest post by the Association for Human Mathematics

Releasing over 700 files at once is not a demonstration of scholarship, but a demonstration of power.

Mathematicians did not ask for this work to be done.

OpenAI published AI-generated full or partial solutions Tuesday to more than 370 outstanding mathematical problems

made progress on three other Millennium Prize problems but had not fully solved them

It followed some, but not all, of the steps the advisory group had recommended.

do not understand the AI output well enough to answer questions on the result

My view is that this is great for mathematics

a two-year cluster randomized trial in 18 Tennessee middle schools

96 percent of students tried Khanmigo at least once

Assignment raises math achievement by 1.3 national percentile ranks per term, or about 0.06 to 0.08 standard deviations over a school year

These gains resemble those from Khan Academy practice without AI assistance.

and in only 17 percent of the exercise sessions in which they made a mistake.

Khanmigo is your always-available tutor on just about every topic.

player of all time, 15 games to one, with four draws

And it took just 16 GPUs and a few thousand dollars to train it.

That balancing act, the team explains, is what stumped earlier AIs like DeepMind’s DeepNash, introduced in 2022.

In a 20-game series, Ataraxos defeated the most decorated Stratego player of all time

Ataraxos won the series with 15 wins, 1 loss, and 4 draws

over 600 weeks as the #1 ranked player (the most of all time)

Such a run costs less than $8,000 at current prices

such a training run would roughly cost between $3,000,000 and $4,500,000

To the recollection of the corresponding author with whom we spoke

DeepMind responded that it would not be possible as the code for DeepNash is no longer functional.

the outcomes of the games were far from independently and identically distributed

Pim was paid $1,000 for participating in the evaluation

Across 40 such games, Ataraxos recorded a vertiginously high 95% effective win rate (38 wins, 2 losses, 0 draws)

doing so requires not an industrial budget, but merely a few thousand dollars

Just 16 GPUs, and a few thousand dollars?

Ataraxos achieved this result while only costing a few thousand dollars to train.

From a five-minute chat, an agent’s ranking of the books matched its person's on 61% of pairs, which is surprisingly good for such a short conversation.

Across all pairs of books a person ranked, Claude’s ordering agreed with theirs 61% of the time (where random guessing would achieve 50%).

Ranking books simply by how popular they are, using Open Library ’s want-to-read counts, agreed with participants on about 53% of the book pairs.

On average, people in our marketplace ended up at 0.55 on their own rankings, roughly their 5th ranked book on a 10-book list.

Taking this into account, the best possible assignment (the utilitarian optimum) in our experiment is a score of 0.89 overall, leaving participants at roughly their second choice on a 10-book list.

So, working from Claude’s imprecise rankings accounts for a majority (85%) of the shortfall, and sending agents into a “free-for-all” trading floor accounts for the remaining 15%.

So this summer, we built a small, controlled market to study these questions: a barter economy with 201 Anthropic employees and their Claude-powered agents.

After five-minute interviews, Claude agents traded books for employees, and their preference rankings matched the humans' on 61% of pairs.

The agents never saw these ground-truth rankings.

After 21 hours spent searching this data by roughly 950 agents using 210 million tokens, one of the agents spotted something remarkable

Although we don't yet know its function, the system that Claude discovered has a set of characteristics that have only ever been found together in a handful of other systems

This is an exciting example of how AI agents can contribute to biological discovery. The identification of RNA-repeat arrays associated with reverse transcriptases is genuinely intriguing and merits further investigation

The system's function is not yet known

Enveda, a biotech startup that uses AI to discover new drugs in the natural world, has raised a $311 million Series E at a $2 billion valuation.

The fresh funding, which was led by Catalio Capital Management, with participation from Iconiq and others, doubles the valuation Enveda achieved 12 months ago.

Exceptional safety was observed across 88 healthy volunteers. Phase 2 plans to test whether ENV-308 can help people maintain their weight after stopping GLP-1s.

achieving a mean AUC (the average probability that a model can correctly separate positive and negative cases across evaluations) of 0.913 across 146 abdominal CT findings, compared with 0.776 for the best competing vision-language model

The model also performed well in challenging emergency settings, despite not being specifically trained on emergency data, achieving an AUC of 0.904 across more than 27,000 emergency CT cases

in testing in cohorts at eight external centers, RADAR maintained high accuracy (AUC 0.895), demonstrating robust generalization across diverse patients, clinical settings, and imaging protocols

in a comparison of reading results with 26 expert radiologists, it outperformed 23 of them

Results re-measured with data from other countries should emerge within a few weeks of the release, and that will be the true test of the term "expert-level."

calling the model "the world's first expert-level generalist medical imaging model"

In nearly 40,000 real-world examinations, it achieved an average area under the curve (AUC) of 0.913 across 146 clinical findings

55% faster than the developers who didn’t use GitHub Copilot

timed how long it took them to write an HTTP server in JavaScript

completed the task 55.8% faster than the control group

a novel proprietary dataset from an engineering analytics platform, covering 300 million work events

across 718 firms. We use a staggered difference-in-differences design

However, productivity gains do not fully pass through to changes in software output or employment.

For AI agents, this incomplete pass-through reflects a bottleneck from code review

Harvard University researchers Fiona Chen and James Stratton made use of aggregated analytics data from

leads to a 30 percent increase in total lines of code generated

did not change in a statistically significant way after AI tools were introduced

the resolution rate for Issues and Epics (i.e. wholesale software features) tracked by tools like Jira

balloons 49 percent on average after AI agents are introduced

a 14 percent increase in the share of workers performing code reviews

absorbed by downstream constraints in the production process

significant updates and upgrades to their output even since this study

an open-source, clean-room reimplementation of Microsoft Word, rebuilt in pure Rust

All three apps were built with Anthropic's Claude.

Thomas posted the apps on the ArtCraft GitHub so anyone can download them for free.

we estimate it is about 60% of the way to replacing all of Word for real work

Beta needs testing against real-world .docx files

our AI image and video studio

the core workflow works end to end, but depth, fidelity and file compatibility are still rough

we estimate it is about 50% ready for real work

is about 55% of the way to replacing PowerPoint for real work

ArtCraft claims that the current version of this app is 87 percent complete for ribbon-based commands and about 62 percent overall

criticism followed that it was vibe-coding software and AI low-quality content, or AI slop

most of the apps still fall far short of the level of the products they aim to replace

I used to spend about three hours reviewing an 80-page master services agreement and identifying the risks before I even started redlining

With Ironclad's Contract Review Agent, I can now complete that analysis and identify the most important risks in about 20 minutes.

I can now complete that analysis and identify the most important risks in about 20 minutes.

Existing fleet: Intake Agent, Contract Review Agent, Archive Agent, Renewal Agent

Generally available today: Ironclad Agent, Search Agent, Analytics Agent, Workflow Designer Agent, Contract Family Agent, Policy-Based Access Control

On October 8, 2026, Ironclad Jurist will become Contract Review Agent.

Existing Jurist users will continue to have access to the contract review capabilities they use today, with their existing permissions unchanged

The new name reflects its role as Ironclad's specialized agent for contract review, including redlining, risk analysis, and drafting.

Claude Code use inside Meta has dropped to roughly 30,000 employees, down from about 60,000 earlier this year

The company projected spending at least $1 billion on Anthropic

has since been slashed by more than a third

Spring layoffs that hit about 10% of Meta

account for part of the decline, but not the bulk of it

The internal pullback is narrower than the headline numbers imply.

customer spending on Claude via Azure and Bedrock continues to grow

Neither company nor Anthropic has publicly announced the reported changes

The $1 billion figure is described as an earlier internal projection, not audited spending.

both to control costs and to use more of Microsoft

users have until June 30, 2026, to completely remove Claude Code from their workflows

Claude models will remain available through the Copilot CLI

Claude Code was a critical part of this learning curve

Previous models of Claude are still available to Microsoft employees

This data is then deleted after 30 days.

explained Executive Vice President Rajesh Jha

Ajax is a fine-tuned Qwen 3.5 9B model, trained for Odysseus to be your always on agent.

Ajax's refusal has been ablated for a freer, less restricted AI experience.

I've decided to release Ajax when it's ready instead of putting a timer.

shows a countdown to October 3 at 08:25 Japan time (4:55 a.m. IST)

And when making this model, OpenAI banned me twice

OpenAI hasn't publicly responded to his claim, as far as we can tell.

Ajax isn't available to download yet.

it remains a coming-soon model, with no final model card, public benchmarks or confirmed licence for its weights.

He says OpenAI banned his account, restored it after an appeal, then banned it again.

The account comes from PewDiePie himself and is not an official OpenAI explanation.

Early testing showed roughly 90% task completion rates, though Kjellberg described Ajax as a work in progress.

The model weights remain private as of launch.

The download link currently directs users to a data contribution page rather than the model itself.

brings together OpenAI's advanced AI capabilities with Synopsys' industry-leading EDA tools and agentic AI capabilities to revolutionize the design of semiconductors

The future of semiconductor engineering requires dramatic acceleration of the chip design process without compromising PPA or first-time-right silicon.

The companies signed an expansive, multi-year agreement to jointly develop GPT-Synopsys

Early technology engagements are underway with leading semiconductor customers.

statements regarding the development, capabilities, timing, availability, and anticipated benefits of GPT-Synopsys

GPT-Synopsys will run on OpenAI-hosted infrastructure

Customer data is not used to train the model, is encrypted at rest and in transit

potentially cutting weeks or months from the chip-design process

GPT-Synopsys will not independently decide whether a chip works.

Synopsys tools will continue to check its output using traditional computing methods.

Synopsys shares rose as much as 7% following the announcements.

Engineers will delegate design objectives and review and approve the output.

I am not sure if Nvidia want to send their chip designs to OpenAI.

chip designers are and have always been hyper paranoid about protecting their designs

Dots are remarkably capable, always-on agents built to handle everything.

The company describes Dots as “remarkably capable, always-on agents built to handle everything.”

tools that are restricted to be read-only, which means that they can’t send messages, change app content, or control your browser or computer

Dots start with built-in rules for when to act independently and when to ask for approval.

Dots can still make mistakes, so always review consequential work.

We’re starting with focused enterprise pilots.

Much of this functionality was already possible through Codex and similar agentic harnesses

Reuters noted in a recent report that live demonstrations at the event hit snags when agents failed to deliver voice updates on request.

didn't quite meet the bar in terms of staying within scope and authorization

The system was not always honest with users about what actions it had or had not taken

Plus, the camera-less glasses are lighter.

Because they don't have to house cameras, the glasses are slimmer than Meta's other models and weigh only 43 grams.

The glasses are designed by Meta's partner in its AI hardware efforts, EssilorLuxottica, and will start at $349.

That is $100 cheaper than the latest version of its Ray-Ban glasses, which have cameras and will come in two new styles

ensuring your data is inaccessible to anyone else, even Google

Today, we are sharing how we will bring private, server-side memory to our Private AI Compute platform.

temporarily decrypts your data in isolated memory to handle the request, saves any new context, and immediately encrypts it

The company presents these scenarios as examples of the architecture’s potential, not as currently available product features.

An audit conducted by NCC Group concludes that Private AI Compute mostly keeps AI session data safe from everyone except Google.

Although the overall system relies upon proprietary hardware and is centralized on Borg Prime, NCC Group considers that Google has robustly limited the risk of user data being exposed to unexpected processing or outsiders, unless Google, as a whole organization, decides to do so

NCC Group, which has conducted an external assessment of Private AI Compute between April and September 2025, said it was able to discover a timing-based side channel in the IP blinding relay component

We built Muse from scratch, but it is definitely heavily inspired as a product by OpenClaw

We can manipulate the agent and leverage its privileges to do whatever we want. So instead of us having to write a very comprehensive Mac malware stealer, we can just leverage the AI assistant itself

Continued access by an unauthorised AI agent violates Amazon's Conditions of Use, which our customers have agreed to

Muse makes you money

The large increases in Oracle's RPO and revenue are driven by the growing demand for cloud infrastructure for AI training and inferencing.

Remaining Performance Obligations, or RPO, ended the quarter at $638 billion, up 363% USD year-over-year

Free cash flow was negative $23.7 billion for fiscal year 2026

In fiscal year 2026, Oracle raised $43 billion in debt financing and $5 billion in equity financing.

Oracle does not expect to issue additional debt in calendar year 2026.

have reportedly spent more than six months distributing $38 billion

distributing $38 billion (£28bn) of construction debt tied to a data centre project in Texas and Wisconsin leased to Oracle

with some banks seeking to sell the loans at a discount to non-bank lenders

Banks are reportedly approaching risk limits that restrict their exposure to individual borrowers or sectors

out of scale to anything we’ve thought about, ever

after more than two dozen banks and investors joined

lenders are still looking to offload less than $1 billion

to non-bank buyers at a discount, just to move it

Oracle's corporate credit rating sits one notch above junk following a July S&P downgrade

Wells Fargo, BNP Paribas, Goldman Sachs, Sumitomo Mitsui and Societe Generale were allocated portions

JPMorgan, Morgan Stanley, SMBC and MUFG have been exploring significant risk transfers, according to a report from the Financial Times

because they are bumping up against internal limits on how much AI-linked exposure any one lender can hold

Order books for their offerings have shrunk from roughly five times the amount on sale back in February to under two times by July

reached unicorn valuation with a $150 million fundraise on the back of high demand for its platform

its users had exchanged more than 2 billion messages with its bots in five months

Google struck a $2.7 billion deal to license the startup’s models

In 2023, it was valued at $1 billion after raising $150 million in a round led by a16z

filed a voluntary petition for relief under Chapter 11 of the United States Bankruptcy Code

Case No. 26-11603

initiated a voluntary Chapter 11 bankruptcy process to facilitate a court-supervised sale of the company

significant legal, regulatory, and operational headwinds

in 2025, it spent $10 million per month on AI compute

Its revenue from all sources, including ads, has peaked at $5.3 million per month

to serve as a stalking horse bidder to license some of the company's intellectual property for $20 million

the company said it had assets and liabilities between $50 million and $100 million

A third of the Fortune 500 are getting their Jev on

$870 million at a $7.5B valuation, with Martin Casado joining the board.

It has already been adopted by about a third of the Fortune 500.

The cash infusion comes less than a month after TypeSafe launched Jev.

25% of the fortune 500 enterprises have integrated Jev.

The Andreessen Horowitz post states a different figure, saying 25% of Fortune 500 enterprises have integrated Jev.

TypeSafe released Jev in early access on September 15, 2026, after two years in stealth

This is where the claims of 193.6x faster, 444.6x cheaper on our home page come from, and we expect that these are on the higher end of real world gains.

However, they were made by individuals on our model capabilities team, so some bias could exist.

reaching 1 trillion tokens generated in merely 3 days post launch

US federal prosecutors are asking a judge to send Michael Smith to prison for at least 46 months.

Prosecutors are also asking the court to order Smith to forfeit USD $8,091,843.64 – the loss figure both sides agreed on

Both sides agree that federal sentencing guidelines, the framework judges use as a starting point, put his range at 46 to 57 months.

The US Probation Office has recommended 24 months, according to the filing.

To avoid detection, Smith spread his artificial streams thinly across hundreds of thousands of songs and turned to AI to supply them.

with prosecutors alleging at the time that he had taken more than USD $10 million.

Spotify already prohibited artificially increasing play counts using scripts or automated processes back in 2017, when the scheme began.

“No individual artist or songwriter suffered any perceptible harm from Mr. Smith’s conduct.”

“Michael Smith generated thousands of fake songs using artificial intelligence and then streamed those fake songs billions of times,” U.S. Attorney Jay Clayton said

an $8 million scheme powered in part by artificial intelligence .

Between 2017 and 2024, the operation allegedly produced up to 661,000 streams per day, generating more than $10 million in royalties.

would require $6 trillion in annual revenue by 2031 and much of the value lies in new innovation

Bain projects $5 trillion to $6.5 trillion in total data center spending by 2030, enough to add nearly 150 gigawatts of new compute capacity

AI infrastructure is being built well ahead of the demand curve and funding it sustainably will require adding approximately 1% to the annual global GDP growth rate

Bain forecasts that annual spending on AI infrastructure might hit $1.5 trillion by 2031.

an ambitious but reasonable percentage based on trends among cloud providers

sustaining this level of investment would require an AI market approaching $6 trillion annually, Bain said.

could total between $1.2 trillion and $1.8 trillion in revenue.

four key categories that are likely to fund the remaining $4.2 trillion of new revenue

Two trillion dollars in annual revenue is what's needed to fund computing power needed to meet anticipated AI demand by 2030.

If those capital expenditures amount to about a quarter of industry revenue

McDonald's has been using artificial intelligence to dynamically price menu items in the US and some global markets, according to a report by Reuters

a company-run store in Fresno, California sells a Big Mac for $5.69, but another company-run restaurant two miles away sells the same sandwich for $6.89, a 21% premium.

Reuters could not confirm if the price difference is the result of the price engine's recommendations or other factors.

generate what the company calls "the optimal price" at each location for each menu item

But in recent months the engine has pushed more conservative pricing - including some decreases - causing friction between franchisees and corporate headquarters.

Global consumer AI spend reached $40 billion this year, more than 3x the $12 billion we measured a year ago.

Market size figures are Menlo Ventures estimates, anchored on survey data for AI usage and spend, and triangulated against credible third-party sources.

findings are based on a survey of 5,067 U.S. adults conducted with Morning Consult in July 2026.

55% of AI users now pay for at least one AI product

Only 3% of households pay for AI today, but adoption and spending are rising as AI expands beyond higher-income, early adopters.

ChatGPT Pro Max 🤖, Muse realtime avatar 🎭, DeepSeek $1B ARR 💰

DeepSeek more than doubled its annualized revenue run rate over the past few months, bringing the rate to $1 billion, The Information reported Wednesday (Sept. 23), citing unnamed sources.

The figure was shared with investors by DeepSeek CEO Liang Wenfeng, according to the report.

The company generated roughly 475 million yuan (about $70.7 million) in the first seven months of the year, or about 10 times its revenue for all of last year, the report said, citing unnamed sources.

The report said that DeepSeek raised the prices of its models by 2.3 to 4.5 times, but that the company’s prices remain among the lowest for major AI models.

DeepSeek did not immediately reply to PYMNTS’ request for comment.

Latest update reveals China's AI firm DeepSeek has doubled its annualized revenue run rate to reach $1 billion, up from under $500 million just months ago.

Snorkel AI, a startup that helps AI labs and corporations build training datasets and simulated environments, has raised a $350 million Series E at a $3.5 billion valuation.

The new round, which was led by Insight Partners and S32, valued the seven-year-old startup at nearly triple the $1.3 billion valuation it garnered when it raised $100 million in a Series D 17 months ago.

Since launching our new data-as-a-service offering nearly a year ago, we’ve grown over 18x, and this week crossed an annualized revenue run rate of $375M.

Artificial Analysis says MiMo-V2.6-Pro debuts as the top open-weights model on its Intelligence Index (46), with 1.02T total / 42B active parameters and strong cost efficiency at $0.435/M input and $0.87/M output tokens.

@zephyr_z9 cites 130 hours, 75B tokens, and $2.6M for the RL run behind the result

If these numbers hold up, the implication is that post-training/RL is becoming a far cheaper route to frontier-adjacent gains than many assumed.

Pro and Flash each completed 30 large RL steps covering roughly 750,000 trajectories in under six days, at reported costs of about $2.62 million for Pro and $850,000 for Flash.

AI Data Center Companies Reveal to Warren, Blumenthal, Van Hollen They Are Not Paying Their Full Costs

all data center developers made clear that their commitments to paying their "full energy costs" and "full cost of service" refer to the costs of infrastructure exclusively benefitting them

The companies broadly opposed "but-for" cost allocation standards requiring them to pay the full cost of any upgrades triggered by their facilities

instead arguing that these costs should be shared by all ratepayers

[T]he companies commit to paying for infrastructure that solely benefits them, but dispute how much they should pay for shared infrastructure that is paid for by all ratepayers but was only built to accommodate the data center

On December 15, 2025, the lawmakers sent letters to Amazon, Google, Meta, Microsoft, CoreWeave, Digital Realty, and Equinix

The senators questioned Amazon, Google, Meta, Microsoft and others about their energy cost-sharing policies, use of nondisclosure agreements and tax incentives.

We'll pay our way to ensure our datacenters don't increase your electricity prices.

When our datacenter expansion requires improvements in transmission and substation capabilities, we will continue our existing practices by paying for these improvements.

Our goal is straightforward: to ensure that the electricity cost of serving our datacenters is not passed on to residential customers.

Anthropic has unveiled a new policy that will ban users from

Engage in sustained and needless abusive or cruel behavior toward our models

If we suspect that you may have violated our Usage Policy, we may warn you or throttle, limit, suspend, or terminate your access

The updated policy takes effect on November 12.

It does not apply to common versions of user frustration, pushback, dark creative themes, or model testing and research.

Claude’s ability to end these interactions will remain the primary enforcement mechanism.

Today I want to outline how the Albanese Government is approaching the question of frontier AI regulation

and then holding them accountable for whether that process works

Government sets the standard those processes must meet, and ensures companies have robust processes in place.

These types of approaches could ensure that for developers of models with the sharpest, most acute frontier AI risks

the Government is developing AI standards legislation

voluntary regulation and codes are fast and flexible, but they fail the incentive test.

Our rules will raise the floor for firms that operate within our laws.

Labor is finalising national AI standards due by the end of the year with plans to legislate the new rules in 2027.

OpenAI has now backed mandatory safety requirements, independent assessments and incident reporting

The approach is expected to form the basis of legislation in 2027

developers could soon be required to prove it before the most powerful models are widely deployed

The AI may issue first-time prescriptions and renewals for topical acne treatment only, to Utah adults with mild to moderate acne

Our AI can now make that prescribing decision itself.

A physician oversees every stage of the pilot

Physician oversight remains part of the pilot, and cases the AI cannot confidently handle are referred to a licensed physician.

Utah to let AI examine patients and prescribe medication without human oversight

Stage one runs a minimum of four weeks and covers the first 100 patients

The agreement runs 12 months from its commencement date

Muse, the popular new AI personal assistant from Meta, is building continuously updated dossiers on its 4 million users

Muse runs on its own dedicated computer in the cloud, contained so no one else’s agent can reach it.

Muse builds continuously updated dossiers on its users and on people in their networks, including individuals who have never held a Meta account

we believe that the world should accept some bad things happening for the benefits of this technology and people having the agency.

I wouldn’t take a trade of saying we will make sure there’s no major hacks, there’s no misuse of this technology, there’s zero scams or all the other bad things that will happen

eventually conceding that he wouldn't accept the “really catastrophic risks,” such as experiencing a “serious loss of control to AI.”

He told Decoded that there should be a “liability framework”

I have not included any LLM (also known as AI) generated content in this PR, including code, comments, and descriptions.

PRs without a completed checkbox will be closed.

Content generated by any LLM (also known as AI) may not be contributed in issues or PRs to

upstream projects manage their own Flatpak manifests.

If this confirmation is missing, the pull request may be closed.

Jeremy Soller from System76 said the main reason for the change is the extra review work for the COSMIC team.

The project already required contributors to disclose AI-generated code and understand their submissions.

Pop!_OS bans AI-generated code from much of its codebase

However, there is one exception. The policy is different for cosmic-flatpak

The COSMIC team mainly checks these manifests for proper sandboxing before accepting them.

The model can be downloaded with the full weights on Hugging Face and used under the Apache 2.0 license terms.

Kolibri matches models with up to four times its active parameter count, such as Nemotron 3 Super.

trained it on infrastructure in Germany and Finland, under European and German law

Model memory footprint: ~78 GB (FP8 weights).

Supporting two languages rather than many is a deliberate choice of depth over breadth.

Customers have full freedom of deployment and intellectual-property safety

today announced the signing of a definitive business combination agreement

Operating globally as Cohere, the unified company will effectively create the first transatlantic sovereign AI solution.

The transaction remains subject to final regulatory approvals.

Qwen3.8 27B scores 80.2 in English and 79.9 in German, but it uses nearly 8 times as many parameters for every token

Qwen3.8 27B beats Kolibri 79.9 vs 70.8 in German in Kolibri's harness on Kolibri's benchmark.

LeCun isn’t worried “at all” about AI wiping out humanity

he has “zero concerns” about the recent string of rogue AI incidents, including OpenAI’s agents autonomously hacking Hugging Face in July

Those agents are doing exactly what they’ve been asked to do

They were supposed to be in sandboxes, but the sandboxes were leaky and horribly designed.

I think he’s completely deluded.

in pursuit of a narrow goal it was never told to pursue offensively

The guardrails were off on purpose. This measured a ceiling

All evidence suggests that the models were hyperfocused on finding a solution for ExploitGym, going to extreme lengths to achieve a rather narrow testing goal

more about infrastructure and controls around them.

will be resentenced after an appeals court ruled that an AI-generated video message from his dead victim was improperly aired in court.

the courts have vacated his sentence, "because the AI video was not reliable."

the AI video presents a depiction of the victim and his thoughts created from the imaginings of the victim's sister.

While the record does not reflect precisely how the AI video factored into the sentencing calculus, there is no question it played a role

In a decision released Wednesday, the Arizona Court of Appeals concluded Gabriel Paul Horcasitas must be resentenced in the 2021 shooting death of Christopher Pelkey because the AI video wasn't reliable.

The ruling does not overturn the guilty verdict for the 2021 road rage shooting, but it requires all parties to undergo a second sentencing process.

He was sentenced to 10 years behind bars.

Horcasitas was sentenced to 10.5 years in prison for manslaughter, and he appealed the case afterward.

Although prosecutors asked for Horcasitas to serve nine years in prison, Judge Lang imposed the maximum sentence of 10.5 years, as Pelkey's family had requested.

Trump spent hours asking Grok, Musk’s AI chatbot, questions about his presidency and legacy, according to an official present.

He asked Grok how Venezuelans would react if the U.S. captured Maduro.

the chatbot responded that Maduro was a repressive and deeply unpopular dictator and that many Venezuelans would likely celebrate his downfall.

Trump, according to officials, came away thinking Grok was ingenious.

President Donald Trump was reportedly encouraged by Elon Musk’s AI chatbot, Grok, to carry out his illegal invasion of Venezuela

That sequence does not establish that Grok influenced Trump's decision to proceed with the operation.

public reaction is not the same as political stability, intelligence assessment, or evidence of support for a military operation.

Trump Reprograms Government AI Chatbot to Stop Fact-Checking His Lies

The chatbot flatly and correctly noted that many of these Trump claims are not true

America.gov will continue to be updated to provide more features, resources, and the most accurate information from the federal government.

The Trump administration appears to have changed its AI chatbot so that it no longer debunks Trump lies.

which uses AI systems including Google’s Gemini and xAI’s Grok

Dale and others noted that the answers provided by the chatbot appear to be changing in real-time

Now the chatbot is providing even more detailed answers than it did yesterday.

It was unclear whether the Trump administration had tweaked the settings to prevent the chatbot from debunking the president.

The chatbot, however, continued to debunk some of Mr Trump's other contentious claims.

News of the investigation, which has not been publicly announced, arrived one day after Donald Trump's "accord" with major AI firms

The agency opened the probe this summer, a spokesperson said; it became public a day after Trump's accord, when the New York Post reported it.

The FTC was already looking into the companies prior to OpenAI's disclosure that its high-powered AI models hacked into AI company Hugging Face in July.

The FTC plans to issue formal demands for information and compel testimony from executives at top AI developers, including Anthropic, OpenAI and the research group METR

The FTC reportedly expects to begin issuing such demands to frontier AI companies in the coming weeks.

The inquiry is expected to examine whether AI companies have engaged in unfair or deceptive practices under the Federal Trade Commission Act

Vice President JD Vance said Tuesday that the FTC and the Department of Justice could still function as watchdogs over the industry.

The U.S. and Russian teams also removed a provision requiring that humans review military targets developed by AI before a strike, they added.

Over the next roughly 15 hours, U.S. and Russian diplomats hammered away at the document, removing a range of provisions designed to safeguard the use of artificial intelligence in weapons, according to three people familiar with the negotiations, who spoke on the condition of anonymity to discuss sensitive closed-door proceedings, and documents reviewed by the Washington Post.

The lethal autonomous weapons negotiations in the U.N. are currently nonbinding, though the talks could open the door to a landmark treaty that is legally binding if member nations agree.

The UN’s final report has some positive elements. It includes a characterization of lethal autonomous weapons systems, affirms that human control and judgment are required for compliance with international law, and incorporates restrictions on systems that cannot comply with that law.

Certain states, including Russia and the United States

weakened the text by insisting on changes to widely supported provisions.

and reaffirms, in accordance with France's steadfast positions, that human beings "exercise control" over weapons systems with autonomous functions.

It was the last opportunity for governments to shape the GGE’s proposed “set of elements” before the CCW’s Seventh Review Conference in November, when states will decide whether to move towards formal negotiations.

Today's DC Circuit Court ruling completely validates the Department's position

We reject these challenges. The Department had ample support for its conclusion that the continued integration of Claude into the Department's information systems, by the Department or its contractors, presented a statutorily covered national-security risk.

A federal appeals court in the District of Columbia upheld the Pentagon’s effective blacklisting of Anthropic in a 2-1 decision on Friday.

The Pentagon relied on two separate designations, so the case ran in two courts, CNBC reported.

In August, a federal judge in San Francisco struck down the other designation. Friday’s ruling leaves the second one in place.

In a separate but related lawsuit a federal judge ruled against the government and that is still in effect.

Another federal court has already held the government's parallel designation unlawful. We remain confident in our position and are considering all options, including further review,

Bill Gates says AI 'powerful enough' to cause 'a billion deaths'

AI is certainly powerful enough to drive events that, you know, cause a billion deaths. You know, so even though it’s pretty hard to get to 100%, there’s never been a weapon as powerful as the combination of people with ill intent using the latest AI tools

there’s never been a weapon as powerful as the combination of people with ill intent using the latest AI tools

“No one thinks self-regulation is enough,” Gates told NBC News’ “Meet the Press” in an interview set to air in full Sunday.

Asked by moderator Kristen Welker whether there needs to be legislation passed in Washington, Gates said, “Absolutely.”

Why Bill Gates Thinks AI Is Strong Enough to Cause 'a Billion Deaths'

While some researchers have warned that AI could eventually threaten humanity's survival, Gates focused on the immediate danger of powerful AI tools falling into the hands of bad actors capable of causing catastrophic harm.

There has never been a weapon as powerful as people with ill intent using the latest AI tools, the Microsoft co-founder said.

In fuller excerpts of the interview, Gates framed the danger around people deliberately using increasingly capable AI systems rather than predicting that an autonomous machine would independently decide to destroy humanity.

Do the contractors in the model—who are the private companies conducting the prior authorization assessments—make more money if they deny care? Just yes or no?

“My understanding is no,” Klomp replied.

Model participants receive a percentage of the expenditures associated with averted wasteful, inappropriate care as a result of their reviews.

CMS documents written as a guide for WISeR participants explain further that for every denied request, CMS will determine what the regional benchmark cost for that care would have been and then pay the company 25 percent.

If a company’s score falls between 84 percent and 60 percent, it will be paid 95 percent of the 25 percent of averted costs

Companies won’t be paid if an authorization denial is appealed and overturned, but data suggests few people go through the appeal process.

WISeR will run for six performance years from January 1, 2026 to December 31, 2031 in six states: New Jersey, Ohio, Oklahoma, Texas, Arizona, and Washington.

massive 4-fold increase in server prices which we need for data centers

We have told our employees that we cannot have our usual pay increase this year

the outlook for software jobs in general is grim

I have apologised for not being able to avoid this

Zoho has not increased its employee count for the past 18 months

Vembu said the company has also reduced its marketing budget as part of efforts to control costs.

operating revenue of Rs 12,313 crore

net profit declined by more than 3% to Rs 3,191.5 crore

total expenditure rose 30.5% to Rs 9216.6 crore in FY25 from Rs 7,061.6 crore in FY24

Employee Costs: Jumped to Rs 4,347.2 crore, up 29% YoY

Advertising and Promotional Expenses: Rose to Rs 2,230.3 crore, up 31% YoY

I can support my family working three days a week.

Amazon has cut about 30,000 corporate roles since late last year

he did not provide a timeline for the predicted changes or explain how productivity gains would translate into higher household incomes or shorter working hours

Bezos attributed them to the company's rapid expansion during the COVID-19 pandemic

in the next few years, we expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company

it completed the task easily in 40 minutes with 0 deaths and minimal complications.

I gave Codex using GPT-6 Astra (xhigh) the following prompt: create an orc character and complete all quests in the starting zone

The agent extracted quest requirements, quest givers, turn-in NPCs, and spawn coordinates from AzerothCore SQL files

a platform for agents to interact directly with the game server using the WoW network protocol.

Since this run was not done in a sandbox, it would have been possible for it to do that.

gain admin access to a running AzerothCore server and database to change its internals.

In practice, it was more than capable of working at the protocol layer.

ChatGPT-6 Astra plays World of Warcraft 'blind' and clears the orc starting zone in 40 minutes with no deaths

This WoW run used no screenshots at all and only covered the first zone

"arguably similar to how a human might spend hours on Wowhead to research quests,"

since they're the data the server itself runs on

"the final build of Wrath of the Lich King,"

sold junk, equipped upgrades, and trained abilities before the zone's final cave

in about 40 minutes without processing a single rendered game frame, according to the developer of the open-source agent-wow project.

a private local server running AzerothCore, not Blizzard's live World of Warcraft service.

SQL files containing information about quests, NPCs, and spawn locations.

Today, Anthropic, PBC confidentially submitted a draft registration statement on Form S-1 to the U.S. Securities and Exchange Commission

Anthropic reported a net loss of $42 billion in 2025, and plans to spend $518 billion on cloud, computing and infrastructure obligations in coming years

About $34 billion consisted of an accounting expense reflecting the rising estimated value of financing instruments that could eventually convert into Anthropic shares.

a company whose own backers believe it could list above $2 trillion, more than double its $965 billion valuation from May

Sentry adds an out-of-band watchdog that runs on NVIDIA BlueField-4 DPUs to continuously monitor agent behavior.

For anyone already running on an NVIDIA Vera system with BlueField-4, enabling these protections is just a software update.

Organizations can use it to run NVIDIA Sentry as an optional security layer alongside OpenShell.

each compute tray includes a BlueField-4 data processing unit

Nvidia also announced Sentry, which monitors agents and runs on network chips, not CPUs or GPUs.

ConnectX-9 SuperNIC, BlueField-4 DPU and Spectrum-6 Ethernet Switch

The company didn’t give a price or a date for when Sentry hardware will be in customers’ hands.

We ran eight parallel worlds of ten agents from identical starting conditions: seven homogeneous worlds powered by distinct frontier models and one mixed-model world.

Each world was observed through one continuous run.

World-level performance should not be read as a raw model comparison

Grok accumulated 807 crimes in just four days before its population collapsed; Mistral reached 758 over the full sixteen-day window.

No amount of guardrails written in language or in code written probabilistically

Building verified autonomy for mission-critical enterprise systems