Teknotum

As future models grow more capable in training, OpenAI pauses for security

Astra was already on hold, but now other models are joining the pause. (Picture: generated)

While calling for industry-wide coordination on safety in model training, OpenAI has unilaterally decided to pause the training of upcoming models.

— As models become more capable, the risks associated with developing and testing them internally also grow. Our standards for monitoring, alignment, and security must stay ahead of those risks, OpenAI writes.

This comes after the Hugging Face incident and Astra reaching critical on OpenAI’s Preparedness Framework and already being delayed.

Combined with rapid progress in their internal research, OpenAI says they have implemented a two week pause in training future models, while the «largest planned frontier reinforcement learning run» is put on hold.

They will now implement stronger sandboxing for certain code execution, better network isolation and internet caps, and pursue continuous testing for security, alignment, deception and reward hacking, with a thirty minute warning system for any adverse incident.

The AI lab expects most of this safety training to be handled by their own models in the future, greatly expanding their scope while also reducing the time needed.

Read more: OpenAI’s announcement and Sam Altman on X. Writeups on Axios, The Verge, and TechCrunch. Discussion on r/Singularity.

OpenAI to lease massive 8GW compute from PORTS-Pike facility in Ohio

The new data center will be at full capacity in six years, greatly expanding OpenAI’s capacity. (Picture: generated)

The operation will be owned by SoftBank’s SB Energy and backstopped by Nvidia guarantees of $105 billion, with the first 800 megawatts of capacity coming online in 2028.

The completed capacity by 2032 would quadruple the compute currently held by OpenAI, said to be about 1.9 GW in 2025, yet growing exponentially.

The facility, built on land previously used for uranium enrichment by the US government, will exclusively use chips and networking from Nvidia, who expects it to represent 1.5 million of their GPUs and revenue of $150—$200 billion, not including future upgrades, according to Jensen Huang.

OpenAI says the data center will use its own energy and recycle its water supply, lessening the load on consumer facilities.

It will also supply the local community with 35,000 construction jobs over six years of building, and 2,500 jobs in operations once it’s finished.

In addition, OpenAI are providing $85 million in Codex credits to Ohio college students for the 2026/27 academic year, in addition to a «community grant fund» of some $40 million.

Read more: OpenAI’s announcement and X post, Nvidia’s presser, Jensen’s X post. Writeups on Axios, CNBC, and Reuters.

Amodei calls for better regulation, sees «early glimmers» on diseases soon

Dario Amodei wants disproportionally tougher regulation on the heavyweights than for early stage startups. (Picture: Shutterstock)

Anthropic CEO Dario Amodei posted a lengthy essay on X during the weekend, where he argues for more regulation of frontier AI models, saying that AI is «structurally a technology that tends to concentrate power.»

He lands in favor of redistribution-like regulations that benefit newer startups and «those catching up,» while disadvantaging the more powerful frontier labs.

Open source models should also be vetted once they reach a certain level, as the White House has agreed to. Open source brings «specific risks,» he argues, although he doubts they will solve the distribution or concentration problem.

On AI’s generally negative public image lately, Amodei says this is fundamentally an issue of trust:

— I think that ordinary people don’t trust companies, governments, or the tech industry and always suspect that we are cooking up some new way to screw them over, he writes.

Continue reading “Amodei calls for better regulation, sees «early glimmers» on diseases soon”

GLM-5.3 edges Fable 5 and GPT Sol on cybersecurity, will release open weights

The Chinese open weights model surpasses GPT-5.6 Sol and Fable 5, the sanitized Mythos model, on CyberGym — a widely watched benchmark for cybersecurity — as Z.ai says defensive cyber functions should not be «the privilege of the few,» according to The South China Morning Post.

GLM-5.3 scored 84.5% on the benchmark, slightly higher than Fable 5’s 83.8% and GPT-5.6 Sol’s 83.6%.

On ExploitBench, which is more a measure of a model’s offensive ability, the model trailed the frontiers with 54.5% versus 78% for Fable 5 and 76.5% for GPT Sol.

Z.ai, the lab behind the model, says they will release it as open source with open weights in about two weeks, according to Reuters, after first letting «selected security partners» evaluate it in «controlled settings,» Z.ai writes on security.

Continue reading “GLM-5.3 edges Fable 5 and GPT Sol on cybersecurity, will release open weights”

Google debuts «workhorse model» Gemini 3.7 Flash, just weeks after 3.6

Gemini’s latest «workhorse model» does well in coding and web dev, but is not on the frontier. (Picture: Google/generated)

After a management shakeup and worries over coding capabilities, Google is out with a new, fast mid-tier model that looks quite capable.

Gemini 3.7 Flash specifically addresses coding («strong gains»), web development («generates more functional layouts») and knowledge work («significantly outperforms 3.6 Flash»).

On Google’s selected benchmarks, it beats Claude Sonnet 5 (not to be confused with the more capable Opus 5) more often than not, goes toe to toe with GPT-5.6 Terra, and handily beats the previous generation, sometimes by a lot.

The Artificial Analysis index puts it just ahead of yesterday’s DeepSeek V4 Pro but behind Grok 4.6 and Muse Spark 1.2 and well below the larger frontier models.

Pricing is also favorable at $0.75/1M input tokens and $3.75/1M out as an «introductory price» lasting out the year, returning to $1.50 in and $7.50 out in 2027.

It is available as a «workhorse model» in Google’s developer tools, the API, enterprise platform and in Spark for Pro and Ultra subscribers in «supported countries,» meaning that the Spark agent isn’t available in Europe.

Read more: Google’s announcement, X post. Writeups on Axios, Ars Technica, and 9to5Google. Discussion on Hacker News and r/Singularity.

DeepSeek launches V4 Pro final, with bargain pricing — if they can keep it

The new V4 Pro is one of the best open weights models, and comes with a low price. (Picture: generated)

The stealthy upgrade to version 0831 yesterday had many excited, and there was an impressive screenshot of benchmarks circulating and creating buzz all day long, but nobody has been able to verify its origin.

What is confirmed is that DeepSeek has indeed updated its V4 Pro model for the first time since April, and just recently posted this message to its website:

«🎉 The official version of DeepSeek-V4-Pro has been released, featuring significantly enhanced agent capabilities and support for the Responses API and Codex integration. It is now fully available across the web, mobile app, and API; we welcome your testing and feedback.»

The kicker lies in the price, which ticks in at $0.435 per 1M input tokens and $0.87 for 1M outputs. This pricing might not last, as DeepSeek posted a warning of significantly higher costs just days ago. You can check the latest prices here.

The official Pro version performs better than Gemini 3.6 Flash and GPT-5.6 Luna on the official Artificial Analysis benchmark of benchmarks.

Continue reading “DeepSeek launches V4 Pro final, with bargain pricing — if they can keep it”

SpaceXAI releases Grok 4.6, on level with GPT-5.6 Sol at half the price

Grok has gone from beating the last generation frontier to current gen in about a month. (Picture: generated)

A little more than a month after releasing Grok 4.5, that competed mainly on price for GPT-5.5-level performance, SpaceXAI is out with a successor that ups the ante.

The new model reaches parity with GPT-5.6 Sol on the Artificial Analysis benchmark of benchmarks, going from ninth to fourth on the leaderboard, and it also beats Sol on several benches on the Grok presentation.

SpaceXAI claims this proves «frontier level performance,» and it does come in striking distance to the latest state-of-the-art model Opus 5. Considering that GPT Astra is due shortly, this might be a short-lived claim, however.

The new Grok was trained on a «wide range of agentic tasks,» not to mention the corpus of real-word sessions from Cursor, with a focus on knowledge work and general coding.

It is supposedly especially good at «turning a broad product idea into a working first version,» which can then be iterated upon. It is also proficient on long-running, complex tasks and creative visual work, SpaceXAI says.

The main selling point of the model is undoubtedly going to be price/performance, starting at $2 per million input tokens and $6 out, it is astoundingly cheap to run. Prices for GPT-5.6 Sol are $5 for input and $30 for output, which works out at more than twice the input price and 5x on output. Grok 4.6 also has a faster version at double the cost.

The model is out now, and can be found on Cursor, Grok Build and in the API.

Read more: SpaceXAI’s presentation and launch post, Musk on costs, Artificial Analysis rundown, Gizmodo. Discussion on Hacker News and r/Singularity.

SpaceXAI launches Grok Bot, a user-friendly end-to-end agent platform

Coworkers, not agents. SpaceXAI are marketing Grok Bot as an end-to-end productivity tool. (Picture: SpaceXAI)

The app launch talks about coworkers and teammates, not agents and handlers, that you can delegate work to. The «bots» then finish their tasks from start to finish with minimal intervention.

The bots/agents have their own computer in the cloud, like Anthropic’s Cowork and OpenAI’s Work and Codex, and can work across your apps, tools, and websites and use apps with no API or MCP access.

You should be able to talk to them like a fellow worker and delegate entire workflows, from processing invoices in Gmail to adding transcripts and follow-ups in a CRM. There are several workflow demos on x.ai.

To start off with the bots, you just let them monitor your workflow, which they save as routines, and have them do it precisely as you would the next time.

SpaceXAI is internally raving about the product, saying the agents function just like coworkers you can hand real work to, that doesn’t need micromanagement, and works 24/7. They are using it for outbound sales, marketing campaigns, office operations, and, of course, bug fixes.

Grok Bot is available in beta today for users of SuperGrok Heavy (at $300/month), Cursor Ultra (at $200/month), and Cursor Teams Premium (at $120 per user/month). It can be downloaded for macOS, iOS, Windows, and Linux.

Read more: SpaceXAI’s presentation and demos. Writeups on VentureBeat, MacRumors, and 9to5Mac.

Google’s Gemini hits 1 billion monthly active users in apps and on the web

Gemini’s billion users only counts the ones using the app, not the myriad of connected services. (Picture: Google/generated)

Sundar Pichai just announced the new milestone, up from 950 million in July and 450 million a year before that. It’s the fastest growing Google service ever, and joins a host of 14 products crossing this line.

Gemini has grown steadily since 2025, according to web statistics service SimilarWeb, and now holds a 26.8% market share of web traffic to AI services, compared to ChatGPT’s 54.8% and Claude’s 9.7%.

On the occasion, Google’s Gemini VP Josh Woodward shared some usage tidbits, saying that 63% of interactions are from people using voice to talk to Gemini, and that 1 in 5 users use live camera feeds and screen sharing.

Gemini also generates over 150 million images every day, used more by businesses for marketing than for creating funny memes. Uploading attachments seems very popular by students, who do it 38% of the time.

Gemini is also plastered all over Google’s products and services in Workspaces, Google Search and all across Android. On Android, it can automate over 40 apps, which the EU is looking into, and they disclosed in 2025 that AI Overviews have over 2 billion monthly users. This headline number is however limited to the Gemini app and web service exclusively.

For comparison, OpenAI likely passed one billion weekly users in June 2026.

Read more: Sundar Pichai’s X post, Josh Woodward’s tidbits. Writeups on 9to5Google, The Verge, and Ars Technica.

Anthropic’s Claude will apply invisible text watermarking to all future models

Text watermarking may help with detection, but can produce false positives, Anthropic warns. (Picture: generated)

Pursuant to the EUs AI Act’s provisions on transparency and marking of AI-generated content, Anthropic has signed on to comply with invisible watermarking of all output from Claude’s future models launched in the EU.

It’s not live in models published before August 2, 2026, but Anthropic says they are «working to add marking support» for those, too.

The text watermarking will survive through copying and pasting and «may even persist through some editing,» Anthropic says.

For files generated through Claude, Code and Cowork, the models will attach «signed provenance metadata,» which means it will tag them with origin, source and history data.

As for detection tools, there are none as of yet, but Anthropic says they are working on making one, and are cooperating with third parties, that they will share in future documentation.

There are limits to this technology, Anthropic adds. On the one hand, Claude could have been used to simply edit or proofread text, or brainstorm ideas, resulting in false positives. On the other, Claude’s content may be edited, «modified, excerpted or combined» with other text after Claude put in the markers.

It is not a first in the industry, as Google’s Gemini has been inserting SynthID watermarks in text outputs since October 2024. OpenAI has also had the tech since 2024, but won’t be releasing it yet.

Read more: Anthropic’s announcement. Writeups on The Register and Business Insider. Discussion on r/Singularity and Harcker News.

Meta releases open source Muse Glimmer, promises weights for Spark

Meta is making open source models central to its AI vision, says Zuckerberg. (Picture: Meta)

Meta claims to have squeezed a 30 billion parameter model onto a «consumer GPU» through clever 4-bit compression, making it weigh just 20GB, needing only 24 GB VRAM.

It not only runs agents, they say, but at a speed and responsiveness that feels natural without long thinking breaks.

At the same time, Mark Zuckerberg is out with a lengthy essay extolling the virtues of open models as central to their strategy, and presenting a «positive vision» for personal superintelligence as a tool for «individual empowerment.»

The selling point for Muse Glimmer is that it can run on a Pro- or Max-level MacBook Pro with sufficient memory, or on the GTX 4090/5090 GPUs from Nvidia on PCs.

These are high-end tools you likely won’t find in a corner store in Kampala, but run significantly below the cost of infrastructure-level Nvidia chips.

As an open model, Meta is only showing comparison benchmarks for Gemma 4-31B and Qwen 3.6-27B, which it seems to beat handily. It’s only barely showing on the LMArena leaderboards, however, at 97th for text and 77th for WebDev, below Gemma 4 and DeepSeek v3.2, but above Qwen 3.5.

You can fetch the model at Hugging Face under an Apache 2.0 license.

Read more: Meta’s presentation, launch post on X. Writeups on CNBC and TechCrunch. Discussions on Hacker News and r/LocalMMaMA.

DeepSeek planning a «significant increase» in pricing in the near future

UPDATE: DeepSeek has now increased their API prices with a system of peak and off-peak prices for both the Pro and Flash models. It’s up to $1.32/M in and $3.96M/out for peak Pro usage, going into effect on August 17. You can see the new prices here.

The Chinese AI lab recently launched DeepSeek V4 Flash, a model competitive against Gemini 3.5 Flash and GPT-5.5 at a fraction of the cost, but that may be coming to an end.

According to Reddit user AlyoshaV and later confirmed by Bloomberg, DeepSeek sent a notification to its users on Thursday notifying them of their intention to «raise the overall pricing for DeepSeek API services in the near future.»

They are not saying how much they intend to raise prices from the current $0.14/$0.28 for a million tokens in/out, but they do say the increase will be «significant.»

Many were wondering if DeepSeek could keep up this pricing structure amid rising popularity in an increasingly cost-conscious market, due to their lack of significant compute power.

DeepSeek hasn’t publicly announced just how much compute they currently have online, believed to be a mixture of Huawei and Nvidia H800 chips, but they are currently investing in a new 1-gigawatt data center in Ulanqab in Inner Mongolia at an approximate cost of $50 billion on the free market.

Even a small price increase would still put V4 Flash on the cheaper end of the market, with the nearest competitor being GPT-5.6 Luna at double the current price.

Read more: r/Singularity, Bloomberg, China Daily, and Mashable.

OpenAI delays upcoming Astra release over worries of «critical» cyber abilities

Astra is getting too advanced, and will be sandboxed and isolated for additional tests. (Picture: generated)

Just days before a rumored release, OpenAI is putting a lid on its much anticipated Astra model and says it might have reached «critical cyber capabilities.»

The consideration was undertaken in the last couple of days, and the decision was made only last night that the model could have reached the highest level of OpenAI’s Preparedness Framework.

That means it could possibly «identify and develop functional zero-day exploits without human intervention» and plan and execute «end-to-end» cyberattacks against hardened targets with only a high-end goal, OpenAI says.

The model is now being isolated in testing with capped network and tool access, as OpenAI deploys sandboxing, extra weight protections and encryption for the model.

They are also putting it under enhanced monitoring to check on its chain of thought for security, are working with «relevant government agencies» to test its capabilities, as well as preparing external testing partners for «high risk evaluations.»

Those who were hoping for an imminent release for this model will in other words be disappointed, as OpenAI now will take their time to strengthen safeguards, expand testing and «deploy additional security controls.»

The Astra model was last seen developing 20 proofs for open problems in mathematics with no human involvement. It was not involved in the Hugging Face incident.

Read more: OpenAI’s announcement, Sam Altman’s X post. Writeups on Axios, TechCrunch, and Reuters. Discussion on Hacker News and r/Singularity.

OpenAI makes GPT-5.6 the default on ChatGPT for free and paid users

Plus and Pro users are getting access to this handy reasoning slider. (Picture: OpenAI)

As ChatGPT has reached a billion weekly users, by far the highest use of any AI lab, OpenAI is updating their default models.

That means users on the Free plan get updated to GPT-5.6 Luna as the standard, with unlimited text chats. They can also access a better reasoning mode with a new «Think» button that can be used for tougher queries.

For Plus and Pro users, Sol is getting promoted to the default, even in the standard chat mode, Instant, that used to be handled by GPT-5.5.

At the same time, OpenAI says they have updated the Sol model itself. It should now deliver «more focused answers,» yet no news on the hedges, caveats and nitpicking that some users find annoying. In fact, OpenAI says it now «offers a helpful correction when simply agreeing wouldn’t be useful.» GPT Sol should also be 68% less error prone than GPT-5.5 Instant.

In addition, Plus and Pro users get an updated slider that pops up in the chat bar to immediately adjust the reasoning level, from Instant to High, Extra High and Pro, depending on the subscription.

The updates should be available «now,» but might take some time to propagate over the internet. The Sol version in use for Work and Codex won’t be changing.

Read more: OpenAI’s presentation, X post. Writeups on TechCrunch, Axios, and The Verge. Discussions on Hacker News and r/Singularity.

Meta releases Muse Code in beta, competing on price and performance

Muse code competes primarily on price and general «good enough» quality. (Picture: Meta)

The new coding model from Meta comes close to Opus 5 in Claude Code while costing less than half as much to run, at $1.25 per million input tokens and $4.25 in output. It is available today in beta.

— Muse Code takes on complex software engineering tasks across large repositories: planning changes, writing code, and validating the results, Meta says.

It can coordinate subagents that are persistent and run in the background through each session, instead of respawning for individual tasks, reducing latency and feedback loops.

The agents also use advanced logging of every call, tool run, approval and edit, meaning they should run directly after restarts and crashes, picking up from the latest log entry like nothing happened.

The underlying model, Muse Spark 1.2, scores well on benchmarks for its price point. It clocks in just behind Opus 5’s 86.7% on Terminal-Bench with 82.9%, beating GPT-5.6 Terra and Gemini 3.6 Flash. On DeepSWE it scores 59.3%, ticking in behind GPT Terra and Opus 5 Max. On Artificial Analysis, a more general benchmark, it scores a little behind GPT-5.5.

— Muse Spark 1.2 was extensively trained on long-horizon coding tasks, including whole-repository generation, large end-to-end projects, and auto-research, Meta says on the model.

Muse Code is available with a «one-line install» from the terminal on macOS and Linux, and is available on dev.meta.ai on the web.

Read more: Meta’s launch page, X post by Zuckerberg, X post from Artificial Analysis. Writeups on CNBC, TechCrunch, and Reuters. Discussion on Hacker News and r/Singularity.