From the wires:

Meta releases Muse Spark Open, that can run on dual $10,000 Mac Studios

You need the biggest guns in Apple’s universe to run this. (Picture: Meta)
Apple and Meta teams worked together on making the new model run on two 512GB Mac Studios with Ultra chips, Zuckerberg and Ternus said.

Meta claims the new model is «the most capable US open-weight model to date,» built with 816B parameters and 42B active ones, with a 1M token window, and comes on a license that allows commercial use.

It scores very well on agentic benchmarks against other open models, but often falls short of the leading Chinese ones even on Meta’s selected evaluations. This also holds up on Artificial Analysis’ tests.

The Mac angle seems a nice addition, but this isn’t your garden variety Mac Mini. It needs two Ultra-equipped Studios with 512GB RAM, which isn’t even available on the Apple Store (it’s coming in late October), that will cost you more than $10,000 each.

Compared to running a server farm, it does rather keep the costs down and competes in a whole other league than «will run on your Mac.»

For mere mortals, the model is available through popular API platforms like OpenRouter and Nebius, and it can be downloaded from Hugging Face today.

Read more: Meta’s presentation, on Hugging Face and Apple’s MLX runtime. Axios, MacRumors, and The Verge. Discussion on Hacker News and r/Singularity and r/LocalLLaMA.

Saudi OpenAI employee arrested for hiding audio recorder in meeting room

A lot of people just got even more worried about spying at the AI labs. (Picture: generated)
The hidden audio device was found in an executive conference room in OpenAI’s San Francisco offices during a routine security sweep on September 30, and the man was arrested by the FBI on Friday.

— We take protecting our technology, data, and people seriously, and as a matter of national importance, OpenAI said on X.

OpenAI used its own AI models to understand the device and trace its origins, and the investigation found that the man himself had used «a productivity tool» and a chatbot linked to his wife to ask questions about OpenAI facilities, meeting rooms, and building recording devices earlier, The SF Standard reports. They also found a second device in his belongings

The incident has lead to a full-court press from the Trump administration, concerned about spying and safety at the AI labs, which it considers a national priority;

Attorney General Todd Blanche said that «theft of intellectual property involving cutting-edge super intelligence technology poses a serious threat to our national security», AP reports. FBI director Kash Patel announced that protecting tech innovation is «essential to safeguarding our nation’s economic strength and national security,» according to the NYT, and Jay Clayton, director of national intelligence, issued a statement saying that OpenAI’s «serious situation underscores why winning the global super intelligence race is a national security prerogative.»

OpenAI were content with an X post to explain the issue and said simply that «our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.»

— At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data, they say.

Read more: OpenAI’s announcement. The San Francisco Standard, AP, NY Times, and Gizmodo. Discussion on Hacker News and r/Singularity

Meta releases Muse Wonder, a model for agents that pushes the frontier

Muse Wonder tests Meta’s subscriber model, and might offer some return on their massive AI investments. (Picture: Meta)
Meta says the Muse Wonder model will soon be powering their popular Muse agent, and will be available for paying customers first.

The model moves the frontier on Metas selected benchmarks, especially on agentic ones, where it is slightly ahead of GPT-6 Astra and Opus 5.5, and it also leads in the professionally minded Vals Harvey’s Legal and Vals Finance Agent v2.

Artificial Analysis has it on par with GPT-6.1 Sol and behind Astra, Fable 5.1 and Opus 5.5 with a score of 52.

Meta also claims Muse Wonder to be its safest model yet, with very strong safeguards against cyber «misuse,» yet leaving defensive capabilities intact. They also say that «alignment is central to how we build personal superintelligence,» and this carries through on this model.

When Meta launches new models, it is usually significant because of their huge user base – these models would get exposed to billions of people through Facebook, Instagram and WhatsApp.

Muse Wonder will however go to subscribers first, then it will power the Muse agent and later go on to be the basis for specialized models. It is unclear whether it will be released widely, opened to Facebook users or get open-sourced.

It is also getting released in the API at a price of $4 per million input tokens, and $20 for output, which is comparable with other frontier models.

Read more: Meta’s presentation, Artificial Analysis ranking. More on CNBC, TechCrunch, Axios, and Reuters. Discussion on r/Singularity and Hacker News.

Lawyers scramble to shut down vibe-coded AAA game ports for the browser

Some games work better than others, and it is currently whack-a-mole with url takedowns from publishers.
Thanks to Claude Opus 5.5’s gains in coding, it can now quickly recompile entire popular games to the web, and lots of AAA titles are currently getting posted online.

That includes games like Modern Warfare 2, Black Ops 2, Halo CE, GTA 5 and Vice City, Half Life and Diablo.

The games are responsive and playable, work just fine in the browser, and are popping up on obscure URLs all over the place.

The games mostly run very well, Kotaku notes, while also saying that they are of course DMCA-protected intellectual works.

That means they can be taken offline with a simple lawyers notice, but Destructoid points out they can be harder to stop than that.

Once anyone can get Claude to port a game to the web in an easy and fast fashion, they start emerging everywhere on the internet, and not always on .com addresses, but privacy-protected and hard to reach ones.

The industry is already hard at work taking websites down, Destructoid writes, but this might of course be better handled at the source — with Claude itself.

Read more: Kotaku, Destructoid, and Medium. Discussion on Hacker News and r/Singularity.

Gemini hops on the persistent agent train, and makes a play for enterprise

Gemini agent can connect to just about anything, and uses MCP for what it can’t. (Picture: Google)
Google launched the Gemini agent today, a persistent cloud-based agent that does stuff for you across your workspace. It is joining Meta, Microsoft, Grok and OpenAI in what is becoming a busy space.

Google says nearly 80% of all Cloud customers use AI products and nearly 90% of the Fortune 100 use Gemini Enterprise.

So the sales pitch for the Gemini agent is that it is already hooked up to Google’s ecosystem, and knows a lot about you and your work and context.

That means Gemini understands your businesses from day one, «from pricing to product portfolios to departmental norms,» and «all the institutional knowledge that makes your company unique.»

As with other agent assistants, it gets to know you better the more you use it, and it is cloud-based — meaning you can access it from anywhere and it keeps working after you close your laptop.

Gemini agent can use the best underlying model for the task at hand, sometimes opting for a faster and cheaper one, other times choosing something stronger. It already works with the Gemini family and Anthropic’s Claude, and they are hoping to add «other leading private and open models in the future.»

They are initially optimizing the agent with a couple of very specific skills for data and ML engineers and general business users — and are specializing Gemini for financial services and legal workflows.

Read more: Google’s (long) presentation, launch post. The Verge, Reuters, and 9to5Google. Discussion r/GeminiAI.

GPT-6 comes to Chat, with preliminary answers and visual «Intelligent UI»

With the rollout of ChatGPT-6 Sol and Luna in chat for all tiers today, OpenAI is also launching much more interactive replies.

In addition, GPT-6 is gaining a new function best described as partial responses, letting the model respond to a query while it is still reasoning, updating along the way as it arrives at its answer.

The bigger deal for OpenAI is however the richer, interactive HTML-like responses that put pictures and diagrams right in the replies.

These can also be interactive and change as you click buttons, sub-heads or navigate a diagram, all generated on the fly.

That means you get maps for planning road trips, workable calculators for splitting bills and clickable charts for history buffs, all laid out like you were reading a presentation on the web.

The update is rolling out today with GPT-6 in chat, with Sol for the paid tiers and Luna for Free and Go. Codex and Work see no changes. The rollout is staggered and might take a while to propagate.

Read more: OpenAI’s presentation, launch post. The Verge, Wired, and MacRumors. Discussion on Hacker News and r/OpenAI.

Internal OpenAI model solves hundreds of open mathematical problems

There were 377 total problems solved, and it could take a long time for the community to process. (Picture: generated)
In what may be a watershed moment, at least in terms of quantity, the same internal model that solved the Navier-Stokes millennium problem has solved a torrent of open problems — and posted them all on GitHub.

— We are entering a new era of discovery now, Sam Altman says on X, followed by OpenAI president Greg Brockman’s comment that we are moving — towards acceleration of scientific discovery and improving quality of life for everyone.

The problems span deep and wide across the field, with solutions on anything from number theory to computer science and mathematical physics to complex geometry. There are so many solutions that OpenAI says their listings themselves are not rankings, in a document that spans 41 pages with as many as ten problems per page.

In OpenAI’s announcement, they say they have been consulting with Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study «to develop best practices» and to «inform how we release these results,» after getting backlash from the mathematical community on earlier problem releases. The group says:

— This release is the beginning, not the completion, of the process of human understanding and the incorporation of the work into mathematical knowledge.

«Many» of the proofs are formalized in Lean, which means they are computer-verifiable, with more to come «as we obtain them,» OpenAI says. Along with the proofs themselves, there are ten summaries of model reasoning and estimates on how much compute was spent to solve each problem. OpenAI says the average time spent was three hours of ChatGPT Pro-level reasoning.

— We want to directly empower scientists with state-of-the-art capabilities and are working to responsibly release the model that produced these results, OpenAI writes.

Read more: OpenAI’s presentation, GitHub repository, PDF overview, and AGMAI’s statement. NYT and Interesting Engineering. Discussion on Hacker News and r/Singularity.

Following the AI Act, ChatGPT will be getting text watermarks in the EU

It’s easy to edit away text watermarks, OpenAI says. (Picture: generated)
Even if the technology is troublesome, OpenAI says they will be adding their invisible watermarks, textGrain, to outputs «over the coming weeks» for users in the European Union.

Article 50 of the AI Act clearly states that «providers of generative AI systems» must «mark outputs in a machine-readable format» and make it detectable as «generated or manipulated.»

The verification of these watermarks is trickier, as OpenAI will only be opening the watermark detector to approved researchers and «expert organizations» to «help improve the technology.»

They also caution that the watermark detection drops profoundly for edited text and are less reliable for shorter outputs.

Watermarks won’t divulge who owns the text or is responsible for it, don’t identify the user or verify accuracy — and the lack of one doesn’t necessarily prove human authorship. OpenAI also add that watermarks don’t reveal the level of assistance, as a user could have used ChatGPT to merely proofread a text, for example.

OpenAI already provides their images and audio with invisible SynthID tags that can be checked at openai.com/verify, but it is unclear if this page will also be detecting text in the future.

Anthropic also complied with the AI Act in August, with many of the same caveats.

Read more: OpenAI’s announcement, X post. TechCrunch, The Verge, and Gizmodo.

Google delivers Gemini 4 Argon in limited release, nudging the frontier ahead

Argon does push the frontier a little, but benchmarks don’t point to a showstopper. (Picture: Google)
Their first fresh frontier model in nearly a year isn’t getting a public release just yet, it is being rolled out to «trusted cyber defenders» and government to test and harden its capabilities first.

The model is built for «complex, long-horizon workflows,» Google says, and is supposedly excels at «real-world software engineering and enterprise knowledge work.»

It is also good at cybersecurity, being able to find, validate and patch vulnerabilities, and the release to testers will be without cyber guardrails so they can test out these defenses.

Argon also slightly pushes the frontier ahead, scoring marginally better than GPT-6 Astra, Opus 5.5 and Fable 5.1 in most benchmarks Google tested with, and is ahead by quite a bit on Arena.ai’s text benchmark, while coming in eighth on coding.

On the Artificial Analysis benchmark it scores a joint second behind Opus 5.5, shared with Fable 5.1 and Astra. It also hallucinates way less, at a rate of just 15% versus Fable’s 69%.

The real-world testing release is in order to protect against misuse, prompt injections, and misalignment, and to harden systems, Google says. They also say they will be monitoring Argon’s chain-of-thought throughout these «pivotal moments of increased capabilities while navigating alignment risks.» Google signed a voluntary safety agreement between industry and the White House just yesterday.

There seems no timeline for general release, but Google says it will roll out to the API and AI Ultra subscribers first, and Argon is available now to members of the Fairwind program. It costs $2 per million input tokens and $10 per million out.

Read more: Google’s presentation. CNBC, Ars Technica, and VentureBeat. Discussion on Hacker News and r/Singularity.

OpenAI launches Dots, a persistent, personal agent that gets to know you

Your friendly neighbourhood agent. Dots could do all your tedious work, and update in the background. (Picture: OpenAI)
Dots is OpenAI’s answer to OpenClaw and Muse. It’s an always-on agent powered by GPT-Astra that can solve tasks and do things on your behalf.

It has its own computer in the cloud and can work just like you would like it to, and the more you use it, the better it gets to know you and how you work.

Dots has something other agents will find it difficult to compete with — OpenAI’s large universe of 4,000 plugins, apps and connectors, including of course services like Teams and Slack.

Users can reach it through ChatGPT on the web or the app, through SMS messages, Slack or Teams. You can even call it on the phone.

It can also be proactive, using the tools and apps you have connected to check for progress and updates while you are offline.

Users can check on Dot’s progress at any time, and set granular limits and permissions. It can’t send messages on your behalf unless you grant it permission.

The vision for Dots is to make it contain teams of multiple agents that collaborate on tasks rather than just the one, giving users their own personal agent swarm.

To begin with, Dots is only available for Pro, Business Premium and Enterprise users (including Edu and Healthcare subscriptions) at no extra cost.

Read more: OpenAI’s presentation. Reuters, Engadget, Wired, and NYT. Discussion on Hacker News and r/Singularity.

OpenAI delays October release of Astra 6.1 due to safety, alignment concerns

GPT-6.1 Astra is postponed until OpenAI can fix alignment and authorization issues. (Picture: generated)
With DevDay around the corner on September 29, promising «20+ releases» in one day, AI followers were getting giddy at the thought of a new OpenAI model, but this now seems off for a while.

Several outlets are now reporting that the scheduled October release of GPT-6.1-Astra has been postponed due to safety and misalignment issues.

The model reportedly had problems «staying within scope,» «authorization» and «communicating» about its work, Saachi Jain, OpenAI’s head of safety systems said in a statement shown to CNN.

— Of course we want to make sure our model development is safe ​no matter whether that’s in the company, or when we ​ship it ⁠to users, Jain tells Reuters, — But when we ship it to users, we have an extremely high bar in terms of safety and alignment.

According to The Wall Street Journal, GPT-6.1 showed elevated levels of deception, and did not always disclose actions it had taken, which was worrying enough to postpone its release.

Read more: The Wall Street Journal, CNBC, CNN, and Reuters.

Anthropic’s Claude-powered biology lab makes its first scientific discovery

The repeating DNA in bacteriophages could possibly reveal a gene-editing method. (Picture: generated)
Anthropic set up its wet science lab in the spring of 2026, and it has already made its first important discovery.

Claude itself has autonomously discovered an enzyme hidden in the DNA of bacteriophages that sits beside a «long array of repeating DNA,» that could work in a similar way to CRISPR — todays most common DNA-editing tool, and a discovery that won the Nobel Prize.

Claude found the enzyme almost entirely on its own, searching through a massive database of DNA sequences for interesting candidates from a human prompt. It took 21 hours, 950 agents and 210 million tokens until an agent made the discovery of repeating DNA sequences. It was then analyzed and tested by scientists in the lab.

Continue reading “Anthropic’s Claude-powered biology lab makes its first scientific discovery”

Anthropic launches Opus 5.5 with better efficiency, alignment and cost

Anthropic is keeping its promise with external evaluators on this model, leading to better alignment. (Picture: Anthropic)
Claude Opus 5.5 is the first release since Anthropic’s pacing pledge, and has been run through external evaluators such as Frontier Design and METR.

It is the best model Anthropic has tested so far on its internal alignment tests and comes with strong safeguards inherited from Fable.

40% cheaper on typical tasks
Opus 5.5 costs $4 per million input tokens and $20 per million in output, 20 percent below Opus 5. Cache reads fall 60 percent to $0.20 per million tokens.

Anthropic says typical workloads cost around 40 percent less overall, while using fewer tokens per task. Output is also more than 30 percent faster.

Continue reading “Anthropic launches Opus 5.5 with better efficiency, alignment and cost”

OpenAI releases GPT-6 Sol and Luna at half the price, with better efficiency

GPT-6 Sol and Luna come just under twenty days after the launch of Astra. (Picture OpenAI/generated)
OpenAI has launched GPT-6 Sol and GPT-6 Luna, bringing technology from its flagship Astra model to cheaper and faster models — while cutting API prices in half.

GPT-6 Sol costs $2 per million input tokens and $10 per million output tokens, half the promotional price of GPT-5.6 Sol.

GPT-6 Luna drops to $0.10 per million input tokens and $0.50 in output.

On OpenAI’s internal factuality evaluation, GPT-6 Sol makes around half as many errors as GPT-5.6 Sol and approaches Astra. At higher reasoning levels, Luna matches GPT-5.6 Sol while costing roughly one hundredth as much, according to the company.

Continue reading “OpenAI releases GPT-6 Sol and Luna at half the price, with better efficiency”

OpenAI says RSI will better alignment and safety, calls for global standards

An AI researcher could also be a safety researcher, but there are risks, OpenAI says. (Picture: generated)
As OpenAI keeps building toward automated AI research and recursive self-improvement, they are saying there are clear benefits to their research, by rapidly accelerating AI progress and bringing down the cost, improving the lives of more people. It also comes with a warning:

— Done without appropriate care and caution, RSI could result in humans losing practical control over AI development, unable to provide oversight on research processes they no longer understand, OpenAI writes in a blog post today.

— From here, AI could become more dangerous, less aligned, and, on the whole, a danger to people, they write.

They are saying they shouldn’t pursue self-improvement unless it can be done safely, while retaining human control, and plan to have a human in the loop for automated research.

An automated AI researcher is likely by 2028 or sooner, they said earlier and presumes it can greatly help with alignment and safety on increasingly capable model releases.

With their warning of the dangers comes a plea for «International standards for safety and security practices» that they think the USA should lead, but it is unclear if their suggestions will be implemented any time soon.

They are also saying that «pacing» is not about «maintaining a predetermined speed. Technically, it is about ensuring that alignment research and deployment of that research stay ahead of capabilities.»

OpenAI is widely believed to launch new models shortly, on or before its DevDay on September 29.

Read more: OpenAI’s blog. More on standards at CNBC, Reuters, and Gizmodo.