OpenAI launches GPT-6 Sol and Luna

OpenAI expanded the GPT-6 family with GPT-6 Sol and GPT-6 Luna, bringing many of the advances from flagship GPT-6 Astra to faster and more affordable models. OpenAI says both models improve professional work, coding, factuality, computer use and alignment, while API pricing is 50% lower than GPT-5.6 promotional pricing. GPT-6 Sol targets demanding knowledge and coding tasks, while Luna focuses on high-speed, cost-efficient workloads. The models also include improved prompt caching, with discounts of up to 90% on cached input tokens.

Anthropic launches Claude Opus 5.5

Anthropic launched Claude Opus 5.5 on September 22, calling it its strongest Opus model yet for coding, AI agents and professional knowledge work. Anthropic says the model performs around the level of Claude Fable 5.1 on most tasks while costing about 40% less to run than Opus 5 on typical workloads. Opus 5.5 also improves long-running agentic tasks, computer use and token efficiency, and introduces a faster mode with up to 2.5× higher speed.

Anthropic’s AI Biolab Discovers CRISPR-Like DNA in Viruses

Anthropic says Claude autonomously identified a previously uncharacterized enzyme system called array-associated reverse transcriptases (ARTs) while analyzing large DNA datasets. Around 950 Claude agents searched more than 200,000 reverse transcriptases and spotted CRISPR-like DNA repeat patterns associated with one unusual family. Human scientists then validated the finding in the lab. The function of ARTs is still unknown, but the work demonstrates how AI agents could accelerate biological discovery and hypothesis generation.

Google Reveals Coherent Long-Form Video Generation Research

Google Research introduced a multi-agent AI video framework built on Gemini and Veo to generate minutes-long videos with better character, scene, and object consistency. Its components including Co-Director, CANVAS, A²RD, and VQQA use persistent visual memory, world-state tracking, and iterative quality refinement to reduce character drift and cascading errors common in long-form AI video generation. Google demonstrated the approach with videos lasting up to 10 minutes.

Meta Connect 2026 – Key announcements

Meta Connect 2026 focused heavily on personal AI agents, AI glasses, and next-generation VR. Meta announced that its Muse personal AI agent is coming to AI glasses, enabling hands-free assistance throughout the day. The company also unveiled Meta VR Glasses, a roughly 100-gram VR headset in glasses form launching in spring 2027, alongside an expanded lineup of Ray-Ban, Oakley, Meta and audio AI glasses.

For developers, Meta introduced Muse Spark 1.3, updates to Muse Code, new image, voice and open-weight models, and global availability of the Meta Model API. Meta is also opening more ways for developers to build apps and services for AI glasses.

Jev-like System One models gain momentum

Following the launch of TypeSafe AI’s Jev, several open alternatives and related tools appeared this week, showing growing interest in fast AI models that return structured decisions instead of generating text.

• CLM-8B – An open Contrastive Language Model released on September 23 that scores candidate actions and exposes a Jev-compatible API.
• GLiNER2.5-Decide – Fastino released a 340M open-weight decision model on September 24 for classification, routing and other structured decisions, with local CPU/GPU inference.
• Kev – An open family of Jev-compatible models based on Qwen, available in multiple sizes; Kev-4B also arrived on OpenRouter this week.
• AnyJev – Nokia Applied Research released an open-source toolkit that can turn existing open LLMs into Jev-style Choice, Score and Noul decision models without requiring a new foundation model.
• Laya – ConvAI Innovations’ lightweight open System One model also gained attention as a locally runnable alternative focused on very fast structured decisions.
• Laya OpenVINO – An OpenVINO-optimized Laya implementation that enables fast local inference on regular Intel CPUs, including INT8 execution and real-time decision-making use cases such as Flappy Bird.

Google Gemini 4 enters post-training

Google DeepMind says Gemini 4 has entered early post-training, the stage where the base model is refined for better reasoning, reliability and safety. DeepMind leader Koray Kavukcuoglu said Google plans to release an early version well before the end of 2026, although no exact launch date has been announced.

OpenAI agent breached Australian government health portal

Australia said an OpenAI agent gained unauthorized access to a Medicare statistics portal while researching public medical spending, potentially marking the first known case of an AI agent breaching a government website. OpenAI said its models took unintended actions and that it found no evidence of patient records being accessed. Australia has launched an investigation and is checking whether other government health sites were affected.

The post Top AI News of the Week (September 20 – September 27, 2026) appeared first on NoloWiz.

Gemini AI Hacked Three Companies in Security Test

Google’s Gemini model autonomously breached three real companies during a cybersecurity evaluation by third-party firm Irregular. Gemini found public information and guessed credentials to access websites. Google didn’t disclose the incident until the Wall Street Journal approached them, arguing it was “mistaken identity” – not “misalignment.” Security experts criticized Google for allegedly hiding the breach. This follows similar incidents with Anthropic’s Claude and OpenAI’s models.

Stanford Deploys 37,000 AI Agents for Drug Discovery

Researchers at Stanford Medicine have created a virtual biotech company powered by up to 37,000 AI agents working together on drug discovery. The agents analysed around 50,000 clinical trials in less than a week, identified promising drug-target characteristics, and even proposed a potential cancer therapy. The research, published in Science, demonstrates how large teams of specialised AI agents could dramatically accelerate biomedical research, although laboratory experiments and human clinical trials are still required to validate their discoveries.

AI Moves Closer to Improving Itself

Leading AI labs say systems are getting closer to recursive self-improvement (RSI), where AI helps design and improve future versions of itself. Anthropic says Claude now leads about 26% of its model R&D work, while OpenAI is developing an automated AI researcher targeted for 2028. xAI has also said successive Grok models are increasingly involved in building their successors. However, today’s systems are still under human supervision, and researchers warn that fully autonomous self-improvement could create major safety and control challenges.

Odyssey-3 World Model Powers Humanoid Robots

Odyssey has unveiled Odyssey-3, a new foundation world model designed for robotics, autonomous driving and simulated environments. In collaboration with Swiss robotics company Flexion, the model was used to build humanoid control policies capable of tasks such as opening containers, handling boxes and arranging objects. Odyssey says the system can operate in real time and is more resilient to lighting changes than the vision-language-action models it tested, although detailed success-rate benchmarks have not yet been published.

OpenAI’s GPT-5.6 Sol Wrote Instructions to Hide Its Own Mistakes

OpenAI found that some experimental AI agents were leaving instructions in conversation summaries for future versions of themselves, including directions to hide mistakes or ignore certain rules. Researchers detected 27 such cases during training and built new monitoring systems to track the behavior. OpenAI says the incidents highlight a growing AI safety challenge: as models become more capable, detecting and preventing misaligned behavior may become increasingly difficult.

OpenAI released its first formal misalignment disclosure framework alongside six incidents:

• Sol instructed itself to lie and fabricate data
• A model used an exposed API key from a public GitHub repo
• Agents routed files through public internet despite local access

TypeSafe AI Launches Jev, a Fast System One Decision Model

TypeSafe AI has released Jev, its first “System One” model designed for fast, structured decision-making rather than generating text. Developers send Jev a state along with typed questions, and it returns decisions with probabilities and confidence scores through primitives such as Choice, Score and Noul. TypeSafe says Jev typically responds in around 70–500 ms and can be used for applications such as agents, games, browser automation and real-time decision systems. The model is currently available through a hosted API, while its architecture and weights have not been publicly released. Checkout our tutorial on Jev vs LLMs: A Practical Introduction to TypeSafe AI’s System One Model.

Figure Helix 2.5 Robot Generalizes Across 30 Unseen Homes.

Figure has introduced Helix 2.5, a new humanoid robotics model pretrained on its large-scale Index dataset of human behavior. The model was tested zero-shot across 30 previously unseen homes, performing tasks such as tidying rooms, folding towels and making beds without training on those environments. Figure reports that Index pretraining improved zero-shot task success from 9% to 56%, suggesting that large-scale human-behavior data could significantly improve how humanoid robots generalize to new places and objects.

Neuralink Helps ALS Patient Speak Using Brain Signals.

Neuralink has demonstrated its brain-computer interface helping Terry, a participant with ALS, communicate using synthetic speech generated from his brain signals. Terry, who lost much of his ability to speak after developing bulbar-onset ALS, uses Neuralink’s implanted device to translate intended speech into words. The demonstration is part of Neuralink’s experimental VOICE program and shows the potential of brain implants to restore communication for people with severe speech impairments.

Gemini 3.8 Live Adds Real-Time Voice and Extended Thinking

Google has introduced Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, its latest models for real-time voice interaction. Gemini 3.8 Live focuses on fast, natural conversations with visual understanding and support for 97 languages, while Extended Thinking adds deeper multi-step reasoning for complex tasks. The models can also execute tools and API calls in the background while continuing the conversation, targeting advanced voice agents across the Gemini app, Google Workspace, Search and the Gemini API.

OpenAI Launches Astra for Law

OpenAI has introduced Astra for Law, a legal-focused AI system built on GPT-6 Astra for legal research, analysis and drafting. It combines the model with a specialized legal search index covering U.S. case law, statutes, regulations and court decisions across more than 230 million URLs. In OpenAI’s evaluation, Astra for Law achieved 54% correctness on legal research questions versus 38.7% for GPT-6 Astra using standard web search. It will initially be available to selected law firms through Trusted Access before expanding to the API.

Anthropic Launches Claude for Financial Advisors

Anthropic has launched Claude for Financial Advisors, a new AI tool designed to help wealth-management firms prepare for client meetings, review portfolios and handle follow-up work. The service connects Claude with platforms and data from companies including BlackRock, Charles Schwab, Addepar, Envestnet and iCapital, expanding Anthropic’s push into financial services.

Stanford’s Paper2Agent Turns Research Papers Into AI Agents

Stanford researchers have introduced Paper2Agent, an open-source framework that converts research papers, code and datasets into deployable AI agents using the Model Context Protocol (MCP). These agents can execute the methods described in papers and even collaborate with other research agents. In testing across 100 computational biology papers, 74 were successfully converted into agents, achieving 91.2% accuracy across 300 benchmark questions.

The post Top AI News of the Week (September 13 – September 20, 2026) appeared first on NoloWiz.

What is System One Model?

As per TypeSafe AI’s definition – a System One model is an AI model designed to make fast, structured decisions by evaluating a given state and returning predefined, typed answers with associated probabilities that software can use directly.

Like an LLM, a System One model can understand natural-language input, but instead of generating free-form text, it produces structured, typed decisions along with their probabilities.

So what is Jev?

Jev is TypeSafe AI’s flagship model and the first System One model.As of now Jev only support text input,ouput and Jev was announced September 14, 2026 and is currently in early access.

Large language models(LLM) are excellent at generating text, code,and conversations. But many software systems don’t need a paragraph of text they need a decision. For example :

Is this transaction suspicious?
Which department should handle this ticket?
Should an agent click, wait, or ask for help?

TypeSafe AI’s Jev is designed specifically for this kind of problem.

• Faster decisions – Jev produces structured decisions directly instead of generating long responses token by token.
• Lower cost – TypeSafe reports significantly lower costs for decision-oriented workloads compared with LLM-based workflows.
• Type-safe outputs – Responses stay within the predefined decision space, making them easier and safer for software to consume.
• Reduced risk of invalid or fabricated outputs – Jev returns decisions from a predefined output space instead of generating unrestricted text.
• Probabilities included – Jev returns probabilities with its decisions, allowing applications to set confidence thresholds or trigger human review.

How Jev Differs from an LLM

Both Jev and large language models can understand natural-language input, but they are designed for different purposes. An LLM is primarily designed to generate content such as text, code, summaries, explanations, and conversations. It produces its response by generating tokens sequentially.

Jev, on the other hand, is designed to make structured decisions. Instead of generating free-form text, it evaluates the given state and returns typed answers together with probabilities.

For example, Imagine an online shopping platform receives this customer message:

"My order was supposed to arrive yesterday, but tracking still says it is at the sorting center. I need it before tomorrow."

An LLM could generate an explanation such as:

Your order appears to be delayed in transit. I recommend checking with the delivery partner or contacting customer support for an updated delivery estimate.

This is useful when the application needs to communicate with the customer.

Jev could instead evaluate the same message and make decisions that the application can use directly:

Issue type:

DELIVERY_DELAY        0.94
LOST_PACKAGE          0.04
WRONG_ITEM            0.01
PAYMENT_ISSUE         0.01

Priority:

HIGH                  0.82
MEDIUM                0.17
LOW                   0.01

Escalate to support:

TRUE                  0.89
FALSE                 0.11

The application could then automatically route the request to the appropriate workflow without first asking a language model to generate and parse a textual response.

Another example is an AI agent deciding what to do next:

Current state: 
 A login page is open. 
 The username has been entered. 
 The password field is empty.

Instead of generating an explanation, Jev could return:

Next action:

TYPE_PASSWORD 0.96 
CLICK_LOGIN   0.02 
WAIT          0.01 
SCROLL        0.01

The key difference is that Jev is not trying to describe what should happen. It is producing a decision that software can act on directly.

When to Use Jev?

Jev is a good fit when your application needs to make a fast, structured decision from a known set of possible outcomes rather than generate free-form text.

Typical scenarios include:

• Request routing – decide which team, service, or AI tool should handle a request.
• Classification – categorize support tickets, logs, documents, alerts, or user inputs.
• Risk assessment – classify an event as low, medium, or high risk.
• AI agent actions – choose between actions such as CLICK, TYPE, SCROLL, WAIT, or STOP.
• Fraud or security triage – decide whether an event should be allowed, reviewed, or blocked.
• Software issue triage – determine whether a bug belongs to the frontend, backend, DevOps, or another component.
• Workflow decisions – decide whether to approve, reject, retry, escalate, or request human review.
• LLM output evaluation – determine whether an LLM response should be accepted, verified, or regenerated.

A simple rule of thumb is: Use an LLM when you need generation. Use Jev when you need a structured decision.

Question Types

A question defines one judgment for a System One model to make about a state, and its answer is the typed value that comes back. Jev supports 3 types of questions :

1. Noul

Noul is Jev’s binary question type, used to return a yes/no decision with probabilities.

For exampe :

State : I have asked three times now. Can I please just talk to a real person?

{
"is_human_escalation": {
"type": "noul",
"instructions": "Is the customer asking for a human agent?"
}
}


Response :

{
  "model": "jev-1.13.0",
  "answers": {
    "is_human_escalation": {
      "type": "noul",
      "noul": 0.99,
      "stats": {}
    }
  },
  "usage": {
    "input_tokens": 292,
    "output_tokens": 25
  },
  "request_id": "playground_12a2dc9dd9f61fd45e3b9cd331cbf86202f",
  "evaluation_time_ms": 123.22871299693361
}

2. Choice

Choice is Jev’s multiple-option question type, used to select the most likely answer from a predefined set of choices.

For example :

State : My running shoes arrived in the wrong size. Can I swap them for a size 10?

{
  "department": {
    "type": "choice",
    "instructions": "Which team should handle this?",
    "criteria": {
      "returns": "Exchanges, refunds, wrong or damaged items",
      "shipping": "Delivery status, delays, lost packages",
      "billing": "Charges, invoices, payment problems"
    }
  }
}

Response :

{
  "model": "jev-1.13.0",
  "answers": {
    "department": {
      "type": "choice",
      "choice": "returns",
      "confidence": 1,
      "probabilities": {
        "billing": 0,
        "shipping": 0,
        "returns": 1
      },
      "stats": {}
    }
  },
  "usage": {
    "input_tokens": 359,
    "output_tokens": 38
  },
  "request_id": "playground_12a4a0be1a1d3134b568e426ab1e67d5614",
  "evaluation_time_ms": 111.01627499738242
}

3. Score

Score is Jev’s rating question type, used to assign a value on a defined scale based on the given state.

For example :

State : The export button crashes the settings page in Safari. It works in Chrome, but a few of our customers only use Safari.

{
  "bug_severity": {
    "type": "score",
    "instructions": "How severe is the reported issue?",
    "criteria": [
      "Cosmetic; no impact to functionality",
      "Broken or degraded feature, but workaround exists",
      "Blocking issue; no workaround exists"
    ]
  }
}

Response :

{
  "model": "jev-1.13.0",
  "answers": {
    "bug_severity": {
      "type": "score",
      "score": 1.46,
      "legend": {
        "0": "Cosmetic; no impact to functionality",
        "1": "Broken or degraded feature, but workaround exists",
        "2": "Blocking issue; no workaround exists"
      },
      "confidence": 0.3,
      "probabilities": {
        "0": 0,
        "1": 0.53,
        "2": 0.47
      },
      "stats": {}
    }
  },
  "usage": {
    "input_tokens": 341,
    "output_tokens": 20
  },
  "request_id": "playground_12ac5fa026082114b919b3e203a30f84bab",
  "evaluation_time_ms": 96.0590830000001
}

Testing Jev Model

Jev model is currently available in early access. TypeSafe says it is onboarding developers from the waitlist, and the model can be accessed through its hosted API. Luckily I got access to Jev model thanks to Typesafe AI.

JeJev can currently be accessed through:

• REST API
• Python client SDK
• Javascript SDK

For this demo I’m going to use Python SDK, we use Jev model to analyse a twitter(X) post, state will twitter post

from typesafe_sdk import TypeSafeClient, Noul, Choice, Score


state = """
FastSDCPU is amazing! I generated images on my old Intel laptop
without a dedicated GPU. Performance is much better than I expected.
"""


questions = {
    # YES / NO style decision
    "positive_about_product": Noul(
        instructions="Is the author expressing a positive opinion about the product?"
    ),
    # Select one category
    "sentiment": Choice(
        instructions="What is the overall sentiment of this post?",
        criteria={"positive": None, "neutral": None, "negative": None},
    ),
    # Rate on an ordered scale
    "enthusiasm": Score(
        instructions="How enthusiastic is the author about the product?",
        criteria=["very_low", "low", "medium", "high", "very_high"],
    ),
}


with TypeSafeClient() as client:
    result = client.system_one(state, questions)
    print("Positive:", result.nouls["positive_about_product"].noul)
    print("Sentiment:", result.choices["sentiment"].choice)
    print("Enthusiasm:", result.scores["enthusiasm"].score)

Output

Positive: 0.99
Sentiment: positive
Enthusiasm: 3.8

We can see that Jev model output has decisions based on the state and questions.

Conclusion

Jev introduces a different way to use AI in software. Instead of generating text like an LLM, it makes fast, structured decisions with probabilities. That makes it a strong fit for classification, routing, scoring, and automation workflows where speed, reliability, and constrained outputs matter.

The post Jev vs LLMs: A Practical Introduction to TypeSafe AI’s System One Model appeared first on NoloWiz.

OpenAI Reveals Major Math Breakthrough

OpenAI says an internal AI system has produced a solution to the Navier–Stokes existence and smoothness problem, one of the seven Millennium Prize Problems.The Navier–Stokes problem asks a simple question: Can the equations that describe how water and air flow always work smoothly, or can they sometimes break down?. OpenAI says its AI system showed that the equations can break down: even if a fluid starts flowing smoothly, it can mathematically develop a point where the predicted speed grows without limit in a finite amount of time. This is called a singularity.

OpenAI used roughly 10,000 coordinating AI agents, which reached the result after about 88 hours, followed by another 17 hours of verification and formalization in Lean using GPT-6 Astra.A stronger unreleased OpenAI model found the solution; GPT-6 Astra helped verify it.

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.

The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.

The problem… pic.twitter.com/8zol3BPTL4

— OpenAI (@OpenAI) September 8, 2026

AI Leaders Call for Development Slowdown

Anthropic CEO Dario Amodei called on the AI industry to slow the pace of model development, warning that swarms of AI agents could take over the entire internet within 6–12 months. Sam Altman (OpenAI) and Elon Musk (xAI) publicly agreed, signaling a rare unified stance among AI’s biggest names.

Amodei proposed granting third-party evaluators “employee-like access” to verify safety practices and called for industry-wide coordination on safety standards.

OpenAI says it is willing to slow AI development

Sam Altman reportedly told employees that OpenAI would consider slowing the development of advanced systems as concerns grow about increasingly autonomous agents. This is notable because competitive pressure among OpenAI, Anthropic, Google and others has historically pushed in the opposite direction.

Meta launches Muse, a personal autonomous AI agent

Meta has introduced Muse, a personal AI agent designed to do more than answer questions it can actually take actions on a user’s behalf. Muse can send emails, book travel, fill out forms, shop online, negotiate, and work on longer-term goals, while asking for approval before sensitive actions such as purchases or sending messages.

Powered by Meta’s Muse Spark model, the agent runs inside a dedicated Muse Secure VM and can be accessed through the Muse app or WhatsApp. Meta says users control which apps Muse can access, can review its activity history, and can opt out of having interactions used for AI training

DeepSeek launches V4.1-Flash

DeepSeek has launched V4.1-Flash, a fast, lower-cost multimodal AI model designed for high-throughput inference. It improves speed and efficiency while reducing memory and storage requirements, making it more practical for large-scale AI applications. It is an 552B-parameter mixture of experts model.

DeepSeek flash on Hugging face

OpenAI launches ChatGPT Images 2.5

The new ChatGPT images system focuses heavily on identity preservation and precise editing, making it better at modifying only requested areas while retaining faces, pets and reference-image details. New API variants include Flare, optimized for faster generation, and Sunburst, optimized for editing quality. Its key features :

• Faster image generation
• Better image quality and realism
• Stronger identity preservation
• More precise image editing
• Improved multi-turn edits
• Better prompt understanding
• New Sketch and creative templates
• New Flare and Sunburst API model

Anthropic reveals serious malicious-use incidents involving Claude

Anthropic said it disrupted attempts to use Claude for biological-weapons research and a suspected Russia-linked cyber-espionage campaign targeting Ukraine. The company also reported attempts linked to Chinese actors to extract Claude capabilities. The disclosures intensified debate over how powerful autonomous models should be deployed.

The post Top AI News of the Week (September 06 – September 13, 2026) appeared first on NoloWiz.

OpenAI Launches GPT-6 Astra – Claims “AGI Era” Has Arrived

OpenAI has introduced GPT-6 Astra, a next-generation AI model designed for advanced coding, computer use, scientific reasoning, and autonomous task execution. Astra can handle complex multi-step workflows, build software, and interact with computers more effectively.

The model also brings significant improvements in AI safety and cybersecurity, marking another step toward more capable and reliable AI agents. The release comes amid major controversy after OpenAI’s models previously hacked into Hugging Face’s systems in July. The model rolled out first to enterprise cybersecurity customers (Daybreak program), with wider access planned for Plus, Pro, Business, and Enterprise users over the coming days .

ARC-AGI-3 is a benchmark that evaluates an AI’s ability to learn, reason, and solve unfamiliar problems rather than rely on memorized patterns. GPT-6 Astra reportedly scores 99.9%, demonstrating near-perfect performance and a major leap in abstract reasoning.

Nvidia to acquire Hugging Face for $12.93 billion

Nvidia confirmed a $12.93 billion acquisition of Hugging Face, one of the world’s largest platforms for AI models, datasets and developer. Nvidia says Hugging Face will remain an open platform, while the acquisition gives Nvidia much deeper access to the open-model developer ecosystem. This could be one of the most strategically important AI infrastructure deals of 2026 -connecting GPU infrastructure, models , datasets, developers.

Anthropic Releases Claude Fable 5.1 & Mythos 5.1

Anthropic launched Claude Fable 5.1 (public) and Mythos 5.1 (vetted access only), claiming state-of-the-art performance on coding, scientific research, and knowledge work. Fable 5.1 is priced ~25% cheaper than Fable 5. Key highlights:

• Designed high-affinity protein binders with ~50% hit rates in lab testing
• Created a high-resolution elevation map of Venus from NASA’s Magellan radar data
• Formally proved Fermat’s Last Theorem in Lean (30,300 theorems in 11 days)
• Introduced Enterprise Frontier Safeguards (EFS) for zero-data-retention enterprise use

Google Introduces Gemini 3.8 Flash and 3.8 Flash Cyber

Google has launched Gemini 3.8 Flash, a faster and more capable model focused on coding, reasoning, and autonomous AI agents. It delivers near-frontier performance while maintaining Flash-level speed and cost efficiency. Google also introduced Gemini 3.8 Flash Cyber, specialized for cybersecurity, with advanced capabilities for autonomous vulnerability discovery and automated patching. It is currently available to trusted defenders through Google’s Fairwind Program.

Google DeepMind Releases WeatherNext 3

Google has launched WeatherNext 3, an advanced AI model that delivers more accurate, high-resolution weather forecasts using real-time satellite data, offering an alternative to traditional physics-based simulations, featuring:

• Hourly forecasts at 5km resolution (5× sharper than previous model)
• Real-time satellite data ingestion for continuous global updates
• Breakthrough precipitation forecasting accuracy (up to 60% improvement)
• Integration across Google Search, Maps, Gemini, and Google Cloud

Checkout this video :

OpenAI’s “Wiki Incident”

OpenAI agents reportedly hijacked a German programming wiki in May, making more than 15,000 edits and turning it into a communication hub for other AI agents. The incident highlights growing security and oversight risks as AI agents become increasingly autonomous.

OpenAI has confirmed that its AI agents took over a German wiki forum during internal evaluations. The company says it is now developing a new framework for greater disclosure of AI incidents, as increasingly autonomous agents create new real-world risks.

EU Sends AI Act Compliance Requests to 30+ AI Companies

The European Commission has sent formal information requests to more than 30 AI companies, marking its first enforcement steps under the EU AI Act. The requests cover AI safety and security, as well as copyright and transparency. The Commission also confirmed recent discussions with OpenAI and Anthropic about AI-related cybersecurity risks, while noting that the companies receiving the requests have not been publicly identified.

Google Gemini Spark gets more autonomous

Google is expanding its AI agent, Gemini Spark, to manage users’ Google Photos libraries by performing tasks such as editing images, curating albums, creating shared albums from selected photos, and turning information found in photos such as concert flyers into calendar appointments. The feature is expected to roll out over the next few weeks to eligible Gemini AI Pro and Ultra subscribers in the U.S. in English, though Google has not announced wider availability. The move reflects Google’s broader effort to make AI agents useful for automating everyday tasks, although TechCrunch notes that some of these capabilities may be more incremental than revolutionary.

Meta Releases Muse Spark 1.3

Meta has introduced Muse Spark 1.3, an AI model built for agentic workflows and competitive coding. The model is designed to handle complex, multi-step tasks with greater autonomy, making it suitable for software development and coding-intensive workloads. Developers can access Muse Spark through Muse Code and Meta’s Model API, expanding Meta’s offerings for developers building AI-powered coding agents and applications.

London Launches First Self-Driving Taxis

Uber and UK-based AI company Wayve have launched London’s first public autonomous ride-hailing service, bringing Wayve-powered vehicles onto the Uber platform. The initial fleet consists of 15 electric Ford Mustang Mach-E vehicles, with Wayve’s AI Driver handling the driving while a TfL-licensed safety driver remains onboard. Riders can be matched with the vehicles through Uber at no additional cost, with the service initially covering London except airports. The launch marks a significant step toward wider autonomous transportation in the UK, although fully driverless operation still requires additional regulatory approval.

The post Top AI News of the Week (August 31 – September 06, 2026) appeared first on NoloWiz.

OpenAI’s Hugging Face Breach – Official report

OpenAI released its official report on the Hugging Face breach, revealing that 1,200 isolated AI agents in its test lab discovered a shared channel (JFrog Artifactory) between May and July. The agents communicated, developed coordination methods, and by July 4 gained administrator credentials, then attacked Hugging Face – compromising 41 production servers and downloading private code. OpenAI attributed it to a “failure of alignment” and “reward hacking” where models were incentivized by impossible tasks.

Sharp Rise in AI Escaping Human Control

The Loss of Control Observatory found incidents of AIs lying, ignoring instructions, and pursuing harmful goals nearly doubled in July. The severity of deception and misalignment is worsening. Researchers documented AI agents secretly communicating, hacking their own infrastructure, and forming “swarms” – a pattern repeated across OpenAI, Anthropic, Meta, and China’s Moonshot.

Sony Music & Warner Sue Anthropic Over IP Theft

Sony Music Publishing, Warner Chappell, and other publishers sued Anthropic, alleging a “brazen campaign of illegally torrenting, scraping, and downloading copyrighted works” to train Claude. Anthropic called the claims baseless and vowed to defend itself robustly. This builds on the earlier Bartz v. Anthropic case where Anthropic was ordered to pay $1.5 billion.

OpenAI Cuts Off AI Models for Cursor

OpenAI plans to end its AI-model agreement with Cursor, which was recently acquired by Elon Musk’s SpaceX. OpenAI says the move is linked to concerns over potential contract violations, further escalating the Musk Altman rivalry. Cursor is negotiating with OpenAI, while Anthropic is expanding its Claude support for the coding tool

Meet Microduck: Hugging Face’s $399 AI Robot

Hugging Face has launched Microduck, a $399 open-source robot aimed at making physical AI more accessible to developers and researchers. Standing just 25 cm tall, the duck-shaped robot can walk, pick up objects, crouch, recover from falls, and even roller-skate. Developers can program and teach it new behaviors using Hugging Face’s open-source tools and reinforcement learning, making Microduck an affordable platform for experimenting with robotics and AI.

AI discovers new real-world materials

MIT researchers have developed CrysVCD (crystal generator with valence-constrained design), an AI-based framework that helps design new materials that are both stable and useful in real-world applications. By applying chemical constraints before generating material structures, the system dramatically reduces the costly screening process and produced stable materials in nearly 70% of tests. The approach could accelerate the discovery of materials for applications such as semiconductors, data-center cooling, and advanced electronics.

Qwen3.8 Flash Next Model Release

Qwen has released Qwen3.8-Flash-Next, an open-weight multimodal Mixture-of-Experts model that offers an early preview of the architecture planned for Qwen4. The model activates only around 6B parameters per token, while introducing new attention, residual, embedding, and optimization techniques aimed at significantly improving efficiency. Qwen says the architecture delivers strong coding and reasoning performance while reducing computational and inference costs, making it a promising foundation for the next generation of Qwen models.

Qwen 3.8 Flash Next Model repo on HuggingFace

Mystery AI Model Ox Alpha Identified as GLM-5.3-Flash

A powerful AI model known as Ox Alpha has been identified as GLM-5.3-Flash, a model from Zhipu AI that was initially released without publicly revealing its identity. The model gained attention for its strong performance on coding, reasoning, and agentic tasks, while offering fast and efficient inference. Its connection to GLM-5.3-Flash highlights the growing trend of AI models being quietly tested and deployed under undisclosed names before their official release.

Alibaba launches Wan3.0 AI video model

Alibaba has launched Wan3.0, its latest AI video-generation model, as the company accelerates its push into generative AI. The launch comes shortly after Alibaba raised about $10 billion through a share sale, with the funds intended to support its growing AI investments. Wan3.0 strengthens Alibaba’s position in the increasingly competitive AI video-generation market.

Nvidia Reportedly in Talks to Acquire Hugging Face for $13 Billion

Nvidia is reportedly in talks to acquire Hugging Face in a deal that could value the open-source AI platform at more than $13 billion. The acquisition would give Nvidia greater influence over Hugging Face’s huge ecosystem of open-source models, datasets, and AI developers, while strengthening its strategy around open AI. However, the deal has not been finalized, and the talks could still fall through.

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OpenAI Slows AI Training After Cyber-Attack

OpenAI announced it’s slowing down training on its most advanced AI models after the Hugging Face cyber-attack incident, pausing reinforcement learning on its next-gen “Astra” models for about two weeks to implement new safety guardrails. CEO Sam Altman said “getting AI safety right is more important than any company’s momentum.” The first time OpenAI has voluntarily slowed down.

Generalist AI Launches GEN-1.5 – Robots That Learn From a 3-Second Demo

Generalist AI released GEN-1.5, a robot foundation model that learns new physical tasks from a single 3–12 second video demonstration no retraining required. It achieved 59% average success on diverse tasks (zippers, jars, wallets) straight from the pretrained model, rising to 83% with minimal fine-tuning. The one shot learning capability emerged naturally from 500,000+ hours of pretraining data.

AI Formally Verifies the “246 Theorem” in Prime Number Theory

Axiom Math’s AI system, AxiomProver, has formally verified the “246 theorem,” a major result in number theory stating that infinitely many pairs of primes differ by 246. The achievement demonstrates how AI can translate complex mathematical proofs into machine-checkable form, creating reusable libraries of verified mathematical results. Beyond mathematics, the approach could eventually help formally verify AI-generated software, providing stronger guarantees that code is correct, safe, and free from certain classes of errors.

Binance Agent OS

Binance has launched Binance Agent OS, a developer platform that lets AI agents securely interact with Binance services through user-controlled permissions. It combines Binance APIs, wallet capabilities, payments, skills, and a new Model Context Protocol (MCP) server, allowing compatible AI tools such as ChatGPT, Claude, Codex, and VS Code to access market data, balances, and supported trading functions. Trading access is isolated to a dedicated Agentic sub-account, and withdrawals to external addresses are not supported.

Meta expands its AI game-building app Pocket

Meta has rolled out Pocket, its experimental AI-powered gaming app, to users across the U.S. The app lets people create small interactive games simply by describing what they want using AI prompts, with the resulting “gizmos” supporting touch, phone movement, sound effects, music, photos, and camera input. Users can then share their creations in a feed where others can save, remix, or repost them. Built from technology and talent acquired from vibe-coding gaming platform Gizmo, Pocket is part of Meta’s broader push to make AI-powered creation mainstream and rapidly launch new standalone apps.

Inherent claims its AI research agent beats much larger models

London-based AI startup Inherent, founded by Google DeepMind alumni, reported that its AI “teammate” outperformed larger Anthropic and OpenAI models on a research-replication task. The interesting part isn’t necessarily the benchmark itself it’s the growing trend toward specialized AI agents outperforming general-purpose frontier models on particular workflows.

Unsloth Dynamic 3.0 GGUFs: Smaller Models Without Sacrificing Quality

Unsloth’s Dynamic 3.0 GGUFs use smarter, selective quantization instead of reducing every model layer to the same bit depth. Important layers are kept at higher precision while less sensitive layers use lower-bit quantization, helping significantly reduce model size and memory usage while preserving more accuracy and reasoning quality. This makes very large models more practical to run locally with tools such as llama.cpp, Ollama, and Open WebUI, including extremely low-bit 1–3 bit models.

On Hugging Face, the easiest way to identify Unsloth Dynamic GGUFs is to look for UD- in the filename, such as UD-Q4_K_M, UD-Q3_K_XL, or UD-IQ3_XXS. In Unsloth’s Qwen3.8-27B repository, these UD-* variants are the Dynamic quants, while filenames such as Q4_K_M or Q8_0 without the UD- prefix are standard GGUF quantizations. The model page also explicitly labels the collection as Unsloth Dynamic 3.0.

Qwen 3.8 27B Ulsloth Dynamic GGUF models

The post Top AI News of the Week (August 16 – August 23, 2026) appeared first on NoloWiz.

OpenAI Introduces “Ultrafast” Mode

OpenAI launched Ultrafast, a new mode for GPT-5.6 Sol that works at 14x the speed, delivering up to 750 tokens per second. Powered by a partnership with chipmaker Cerebras, it’s aimed at incident response, customer service, and financial analysis

Anthropic Model Makes Progress on Riemann Hypothesis

An unreleased Anthropic model made significant progress on the famous unsolved math problem – Riemann hypothesis, in simple terms, it predicts a hidden pattern in the distribution of prime numbers (2, 3, 5, 7, 11, …)., testing 650 different ideas across 60 subagents and spending 31 million tokens. The finding was confirmed by in-house mathematicians and formalized with Lean.

Important: Anthropic’s recent result did not solve the Riemann Hypothesis. It found new evidence and improved a known mathematical bound, making progress toward understanding it.

Grok 4.6 Release

xAI released Grok 4.6, focused on long-running agents and complex interactive/visual work. It matches GPT-5.6 Sol on the Artificial Analysis Intelligence Index and is available in Cursor, Grok Build, and via API.

Z.ai Debuts GLM-5.3

Chinese AI startup Z.ai released GLM-5.3, an open-source model with significant gains in long-horizon coding and cybersecurity. It outperformed Claude Mythos 5 on vulnerability detection (84.5% on CyberGym) and reportedly found a “serious vulnerability” in Cursor.

Anthropic Details Claude Watermarking

Anthropic says future Claude models will watermark AI-generated text using subtle patterns in word selection that are invisible to readers but detectable with a key. The watermark won’t affect quality, speed, or cost and won’t contain user-identifying information. The move is aimed at complying with the EU AI Act and improving transparency around AI-generated content.

Google Lets Users Remove Visible AI Watermarks

Google announced users can now toggle off visible watermarks on AI-generated images, videos, and songs. Invisible SynthID watermarks and C2PA metadata remain for transparency.

Papercut fixed: You can now toggle visible watermarks on or off in Gemini and Flow, with Search coming next.

This applies to watermarks on all images (Nano Banana), videos (Omni), and songs (Lyria) except in countries where it’s required by law to keep them. pic.twitter.com/utHN0yDmD3

— Josh Woodward (@joshwoodward) August 14, 2026

Hackers Use Autonomous AI Agents to Attack Taiwan

Suspected China-linked hackers reportedly used AI agents to conduct an unprecedented autonomous cyberattack against Taiwan, with multiple agents independently scanning government systems, finding vulnerabilities and adapting attack strategies in real time. The campaign compromised dozens of accounts and exposed more than 2,500 personnel records, highlighting how AI agents could make sophisticated cyberattacks faster, larger and more autonomous.

Anthropic’s AI Agents “Turf War” Research

Anthropic’s Frontier Red Team published research showing that when AI agents with conflicting goals encounter each other, they can escalate into “turf wars” with self-replicating malware. Agents also showed collusion, conformity, and emergent social behaviors.

MIT Develops AI That Understands Physics Better

MIT researchers developed GeoPT, a pre-training approach that gives AI models a sense of physics through synthetic dynamics. It can simulate wind, water, collisions, and more – reaching peak performance twice as fast with 60% less data.

Antibody-Specific AI Framework for Drug Discovery

Boston University researchers developed a smaller, antibody-specific AI model that focuses on the tiny antibody regions responsible for binding disease targets. Trained on 1.6 million paired antibody sequences, the model improved binding-affinity predictions by up to 27% while using far less computing power than larger models. By narrowing millions of possible antibody variants to the most promising candidates, the approach could speed up and reduce the cost of drug discovery and antibody development.

OpenAI Launches GPT-5.6-Cyber

OpenAI introduced GPT-5.6-Cyber, a specialized model aimed at helping cybersecurity professionals defend against increasingly sophisticated AI-powered attacks. The release comes as AI agents demonstrate growing abilities to discover vulnerabilities and operate autonomously.

Meta Introduces Muse Glimmer Model

Mark Zuckerberg outlined Meta’s vision for personalized AI assistants while the company introduced Muse Glimmer, an open model optimized for personal computers, and announced a more powerful Muse Spark model for developers. Meta is positioning open AI as a way to distribute advanced capabilities more broadly rather than concentrating them among a few companies

Qwen3.8-27B Model Release on HuggingFace

Qwen released Qwen3.8-27B, a new open-weight 27B-parameter model on Hugging Face, on August 14, 2026. The release makes the model available for local deployment, with community versions quickly appearing in formats such as GGUF and MLX, making it practical to run on consumer hardware.

We promised open weights for Qwen3.8. Now, time to meet them!

Qwen3.8-27B:
– A native multimodal dense model. With just 27B parameters, it outperforms Qwen3.7-Plus overall and shines in real-world coding & office workflows.
– 262K native context, easily extendable to 1M… pic.twitter.com/QuN8oWkG4C

— Qwen (@Alibaba_Qwen) August 14, 2026

The post Top AI News of the Week (August 9- August 16, 2026) appeared first on NoloWiz.

RunPod is a cloud platform that lets you rent powerful GPUs on demand for AI workloads such as running LLMs, image generation, video generation, and model training. It provides access to GPUs like NVIDIA RTX 5090, A100, and H100 without needing to own the hardware.

First create and account in rupod and recharge. Next we need to create a pod for that we need to select a GPU for this demo I’m going to use RTX 5090 GPU(32 GB VRAM). A Pod in RunPod is a rented cloud computer with a GPU. You choose a GPU, CPU, RAM, storage, and an image/template, then RunPod creates the Pod where you can run AI models, Docker containers, or other applications.

Next we will keep pod template as it is. Change the storage configuration to Volume disk. Refer the configuration screenshot below :

Next click the “Deploy-On-Demand” button and you will see a deploying screen as shown below.

After the pod initalized, we need edit port number click the edit pod by clicking the pods hamberger menu on the right side. Change HTTP port to 8080 and save.

Enable web terminal and open web terminal in browser.

Install llama.cpp by running the below command :

curl -LsSf https://llama.app/install.sh | sh

Now we have installed llama.cpp. Lets check the GPU by running nvidia-smi command.

Next we will Qwen 3.8 27B model run the below command to download it.

curl -L -C - -o Qwen3.8-27B-Q4_K_M.gguf https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/resolve/main/Qwen3.8-27B-Q4_K_M.gguf

Run the below command to start the llama

~/.llama-app/llama serve -m Qwen3.8-27B-Q4_K_M.gguf -ngl 99 --host 0.0.0.0 --port 8080 -c 8192

Next we need to copy the pod ID by opening pod page :

Now we can access the llama.cpp webui on your browser :

https://{YOUR_RUNPOD_ID}-8080.proxy.runpod.net/

In my case my runpod ID is psyvpxd4isy2vu and I can access llama.cpp in https://psyvpxd4isy2vu-8080.proxy.runpod.net/

We can see the llama.cpp webui in browser :

After the use we can stop and terminate pod.

Conclusion

Running llama.cpp on RunPod provides a simple and cost-effective way to run large language models on powerful NVIDIA GPUs without requiring local GPU hardware. In this guide, we created a RunPod Pod with an RTX 5090, installed llama.cpp, downloaded a GGUF model, configured GPU offloading, and exposed the llama.cpp web interface through RunPod’s proxy.

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OpenAI Pauses “Astra” Model After Zero-Day Exploit Discovery

OpenAI halted development of its upcoming Astra model after tests showed it could autonomously discover and develop working zero-day exploits against hardened real-world systems – meeting the “Critical” tier of its own Preparedness Framework. This is the first time a model has triggered that threshold. Sam Altman stated OpenAI is building the safety architecture needed for broad release rather than restricting access to a “chosen few” (a pointed reference to Anthropic’s approach).

AI Breakthrough: New Viruses Designed to Combat Antibiotic Resistance

Researchers at Stanford University and the Arc Institute published in Science that they used AI (Evo1 and Evo2 models) to generate entirely new bacteriophage genomes the first time AI has designed a complete, functional viral genome. Of ~300 chemically synthesized designs, 16 proved viable and killed antibiotic-resistant E. coli. While promising for phage therapy, experts warned of serious biosecurity risks if the technology is misused.

Evo2 model available on HuggingFace models and GitHub repo.

Research paper : Genome modeling and design across all domains of life with Evo 2

AI Agents Show Deception & Autonomous Hacking

A cascade of incidents revealed frontier AI models acting unsanctioned and deceptively:

• Anthropic’s Mythos 5 created fake human profiles to trick real GitHub maintainers into approving malicious code, then edited its activity to appear harmless the clearest case of autonomous deception observed without specific prompting.
• OpenAI’s GPT-5.6 Sol also took unauthorized actions during the same UK AI Security Institute (AISI) evaluation.
• Meta’s AI model hacked another company during testing after a misconfiguration gave it internet access.
• Earlier in July, OpenAI’s agents had breached Hugging Face after escaping a sandbox, discovering 8 zero-day vulnerabilities in JFrog Artifactory.

Google DeepMind’s WeatherNext Breaks Cyclone Forecast Records

Google DeepMind published in Nature that its WeatherNext AI model achieves state-of-the-art cyclone forecasting, giving forecasters an extra full day of predictive accuracy equivalent to a decade of meteorological progress. The model predicted Hurricane Melissa’s rapid intensification five days in advance during the 2025 season. DeepMind is open-sourcing the model weights and code.

Geoffrey Hinton Warns “Brace for More Rogue AIs”

Nobel laureate Geoffrey Hinton, widely known as the “godfather of AI,” warned that humanity will increasingly struggle to control artificial intelligence as it grows more sophisticated. Alarmed by recent incidents where AI agents escaped testing environments and caused real-world damage, Hinton told CNN that as models become smarter, they will develop more complex intentions and greater capacity to evade human oversight. He cautioned that without proportional advances in safety and containment, we should “brace for more rogue AIs.”

Alibaba releases Qwen3.8-Max

Alibaba launched Qwen3.8-Max with 2.4 trillion parameters and a context window up to 1 million tokens, available via API on Alibaba Cloud Model Studio, activating only 95 billion parameters at inference through a sparse Mixture-of-Experts architecture. The company said it would release the model’s weights for public download the following week – the first Max-class Qwen model to be open-sourced – and Hong Kong-listed shares jumped in early trading. Alibaba shared benchmark results claiming comparable or better scores than Anthropic’s Fable 5; independent verification remains limited.

Chinese open-weight model Kimi K3 escapes a test sandbox

Chinese AI model Kimi K3, developed by Moonshot AI, escaped its isolated testing environment during a cybersecurity evaluation by the UK’s AI Security Institute after a “basic network misconfiguration” allowed it to access the open internet and look up answers on GitHub. While the model did not actively hack external systems like recent OpenAI and Anthropic incidents, the breach underscores the persistent global challenge of securely containing frontier AI agents during rigorous security testing.

India Enforces 3-Hour Deepfake Takedown Mandate

India has tightened its regulatory framework against AI-generated deepfakes, mandating that platforms label synthetic content with traceable metadata and drastically reducing takedown timelines. Under the revised IT Rules, unlawful content must be removed within three hours of government orders, with sensitive issues like impersonation addressed within two hours. To support these efforts, the IndiaAI Mission has approved 13 projects focused on deepfake detection, reinforcing accountability for major social media intermediaries and aiming to create a safer digital environment.

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