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HP has released a high-priority security notice for enterprise administrators and IT managers regarding CVE-2025-31648, a firmware vulnerability affecting many Intel-based workstations. This issue is found in the Intel processor microcode and could allow attackers to gain higher access rights in certain situations.  

As of March 2026, HP launched the final and urgent remediation phase for business-class workstation fleets, including the Zed by HP and Elite series. This alert stresses the need for prompt action and outlines fleet-wide remediation procedures.  

Technical Summary: CVE-2025-31648 

The vulnerability results from improper handling of values in processor microcode during critical system operations. It manifests when the system startup code interfaces with System Management Mode (SMM), a privileged hardware-controlled environment.  

Intel rates the base severity as low due to the attack’s complexity; nonetheless, the risk is grave for secure environments. Failing to act promptly may leave systems exposed: an attacker with privileged local access and deep knowledge of microcode could bypass normal security limits because this is a firmware-level vulnerability. Standard OS-based endpoint detection and response tools cannot detect it.  

Affected HP Workstation Platforms 

HP’s Security Advisory confirms that the vulnerability impacts several generations of Intel-based hardware currently deployed in enterprise fleets:  

• Z by HP Workstations: Models Spanning the G8, G9, and the Latest G11generations, (including Z2, Z4, Z6, and Z8 Towers).  

• Elite book and elite desktop series Conover business class systems utilizing 12th through 14th gen Intel Core processors  

• HPE SimpliVity and ProLiant nodes: certain workstation adjacent server nodes used in Edge compute environments  

The Resolution: Firmware And Microcode Updates 

HPE is fixing CVE-2025-3164A by releasing BIOS and UEFI firmware updates that will include the latest Intel Platform Update (IPU/2026.1) microcode.  

Fleet administrators must follow these steps to resolve the issues:  

Utilize HP Client Management Script Library (CMSL) or Microsoft Endpoint Configuration Manager to audit BIOS versions across the fleet.  

1. Acquire SoftPaqs: HP has released specific SoftPaq bundles for each affected model. These are available via the HPE Support Site or HPE Image Assistant Tool.  

2. Validate the Microcode revision after updating. Verify that the Microcode version meets the requirements outlined in Intel Advisory Intel SA-01399.6.  

Strategic Mitigation for Fleets 

In addition to applying immediate patches, HPE strongly urges reinforcing workstation security by enabling these hardware-based features without delay.  

• Enable HP Sure Start: Ensure the self-healing BIOS feature is enabled to protect against unauthorized firmware changes during updates.  

• Strict Local Privilege: Because the attack requires a privileged user, enforce a strict least-privileged model at the operating system level to help prevent such attacks.  

• Implement Secure Boot: Check that UEFI Secure Boot is enabled to keep the system secure from start-up through operating system launch.  

Conclusion:  

Although CVE-2025-31648 is difficult to exploit, it represents a serious breach of hardware trust. Organizations with large workstation fleets must act without delay and update to the February or March 2026 firmware versions. Immediate updates are essential to maintaining long-term system security. 

Source: Intel Processor Firmware February 2026 Security Update 

Intel Xeon 6 processors, formerly known as Sapphire Rapids, are architected with enhanced security as a primary design objective. Each single-socket (1S) processor features 136 PCIe 5.0 lanes, surpassing the typical 128 lanes available in competing solutions. The 6700P and 6500P series, introduced in early 2025, target compute-intensive workloads, AI operations, and high-performance computing environments in U.S. research laboratories and enterprise data centers.  

Main Security and Performance Features 

• Security against firmware and update processes: the platforms integrate seamless firmware update (SFU), enabling updates without system reboots and minimizing operational disruption. Security capabilities include Intel Trusted Domain Extensions (TDX) for Confidential Computing and Software Guard Extensions (SGX).  

• Optimized High Speed I/O: with 136 PCIe 5.0 lanes on single-socket designs, these processors develop a 6% increase in I/O capacity relative to competing products, supporting enhanced connectivity for NVMe storage, network interfaces, and hardware accelerators.  

• Integrated AI acceleration: Intel Advanced Matrix Extensions (AMX) support up to 2048 FLOPS for INT8 precision and 1024 FLOPS for BF16/FP16 workloads. This integration optimizes the process of our AI-driven security network analytics and anomaly detection workloads.  

• Performance and memory bandwidth: The processors support DDR5 at 6400 MT/s and multiplexed rank (MCR) DDR5, offering over 37% higher memory bandwidth than standard RDIMMs.  

• Self-boot capabilities: Intel Xeon 6 processors can boot independently without a platform controller hub (PCH), enabling an autonomous CPU boot process.  

In research environments, these enhancements deliver significant gains in AI storage performance and in high-throughput, low-latency workloads, utilizing up to 128 cores per socket.  

Telsium 6 processors feature a flexible dual-architecture design with P-cores for chaining tasks and E-cores for scalable, high-density workloads on the same platform. Choose up to 288 E-cores for strong performance per Watt in cloud-native applications, or high core count, high-frequency P-cores for AI and high-performance computing. This architecture separates tasks to optimize both performance and efficiency, delivering up to two to three times better results.  

Intel Xeon 6 P-cores (Performance-cores) 

Intel Xeon 6 processors with P-cores deliver strong performance per core, with more cores, double memory bandwidth, and AI acceleration in every core, offering twice the performance for AI and HPC tasks. These processors outperform general-purpose CPUs on compute-intensive workloads such as AI inference and ML. They are also well-suited for public cloud workloads, with better performance per vCPU for floating-point operations, transactional databases, and HPC with AI inferencing. Intel Xeon remains a top choice for data processing on leading AI accelerator platforms.  

• AI acceleration is built into every core. Intel AMX boosts inferencing for several model types, letting each core handle up to 2048 floating-point operations per cycle for INT8 and 1024 for BF16 or FP16.  

• You can increase memory throughput with MRDIMM, which delivers over 37% more bandwidth than RDIMM and reaches up to 8,800 MT/s. Both core types also support DDR5-6400 high-speed memory.  

• You can use up to 128 cores per socket and up to 504 MB L3 cache with low latency. Intel AVX-512 is available with P-cores to accelerate vector math for HPC and AI workloads.  

Intel Xeon 6 E-Core (Efficient-Cores) 

Intel Xeon 6 processors with Efficient cores are designed for high core density and strong performance per watt. They are especially useful for cloud-scale workloads that need high task-parallel throughput, compared to the second-generation Intel Xeon Scalable processors, which are common in today’s data centers and are good candidates for performance-per-watt upgrades. Intel Xeon 6 processors with e-cores can deliver over 2.6 times better performance per watt. Their efficiency also makes them a good fit for settings with limited power, space, or cooling. Intel Xeon 6 processors with E-Cores can:  

• Replace 4 servers based on second-gen Intel Xeon scalable processors with just one server while keeping similar performance.  

• Combine three racks of systems with second-gen Intel Xeon Scalable processors into a single rack.  

• Support AI inferencing (making predictions using trained AI models) and vector operations using Intel Advanced Vector Extensions (Intel AVX-512), as well as new features like Vector Neural Network Instructions (VNNI, which help optimize AI tasks) and fast convert functionality for lower precision number formats BF16 and FP16.  

• Provide up to 288 cores per socket, up to 216 MB of L3 cache, and very low latency even with large L3 access sizes.  

Shared Architecture Features 

Compatibility: Both types use the same platform and socket, enabling flexible infrastructure.  

AI acceleration: both have built-in acceleration using Intel Advanced Vector Extensions instructions that improve data processing for AI and Intel Advanced Matrix Extensions/Vector Neural Network instructions (AMX/VNNI, which optimize complex AI computations).  

Security: Both offer advanced security features, such as Intel TDX, for confidentiality. Intel Xeon 6 processors with p-cores and e-cores are efficient because they deliver scalable performance per workload as server workloads increase, with almost linear power and performance across a wide range of workloads. For intensive workloads, this means power is used efficiently to finish tasks faster in cloud or shared computing environments. This efficiency level means servers use only the power they need when busy, helping lower costs when they are not fully used. These processors also support sustainability through system-wide power management and telemetry, which help improve performance per watt in each application and reduce overall energy consumption.  

Source: Intel® Xeon® 6 Architecture – Performance and Efficiency Cores

AMD’s latest ROCm update adds support for new Ryzen APUs and enhances local AI features.  

This consistent progress in ROCm improvements demonstrates how local AI deployment is rapidly gaining power.  

These software advances stem from AMD’s focused efforts over recent years to improve the ROCm stack, particularly for Edge AI. At CES2026, for example, AMD introduced ROCm 7.2.7, which supports the new Ryzen AI 400 Gorgon Point APUs. Building on this, the company has also improved local model performance, which we’ll cover soon.  

In addition to these hardware advances, AMD has prioritized seamless ROCm integration with tools like Comfy UI, an image generation tool that delivers a 5-fold performance increase in ROCm 7. The company has also expanded ROCm support for consumer products, effectively doubling Ryzen and Radeon compatibility over the past year. Together, these moves illustrate how AMD’s software strategy aligns with its consumer objectives.  

Reflecting this increased user base, AMD has introduced smooth integration with the ONNX path for inference and training, targeting Windows AI users and OEMs. Additionally, ROCm is now compatible with PyTorch on Windows, and the Rock software package, an open-source platform from HIP and ROCm. Through these steps, Windows is becoming a key platform for ROCm as AMD drives local AI adoption into the mainstream.  

These technical and strategic improvements now enable local AI inference on consumer hardware to nearly match the quality of cloud-based models. For instance, AMD compared open-source models like GPT running on Ryzen AI Max Plus APUs against cloud-based counterparts. According to AMD, results show that for benchmarks similar to GPQA Diamond and MMLU, local and cloud performances are largely comparable, highlighting how much edge AI has improved through ROCm and new hardware capabilities.  

Source: AMD ROCm 7.2.2 Adds Support for Ryzen AI 400 CPUs & Unlocks Faster Local Inference Performance 

Meta is creating a new Applied AI Engineering Group in its Reality Labs Division to speed up how AI is added to products and infrastructure with a focus on generating revenue quickly. Vice President Maher Saba will lead the team, which reports to CTO Andrew Bosworth. The group aims to connect research and product development through a flat structure, enabling each manager to oversee up to 50 engineers to move faster.  

Key Details of the Tactical Pivot  

• Purpose: The goal is to make AI models faster and more efficient using real-world information and feedback. Meta aims to create a cycle in which models continually improve across social platforms, smart glasses, and Meta AI.  

• The group comprises two teams: One develops internal tools and interfaces. The other manages data pipelines, model evaluation, and oversight.  

• The Applied AI team will collaborate with the Meta Super Intelligence Lab, led by former Scale AI CEO Alexander Wang, to accelerate the development of models such as Avocado for text and Mango for images and video.  

• This shift prioritizes immediate AI monetization, using data to improve ad targeting and engagement across Meta’s platforms, as well as to support AI-powered wearable products.  

• This pivot comes after Reality Labs’ major losses and aligns with Meta’s planned significant AI computing investments for 2026, marking a shift from metaverse to AI-powered wearables.  

This reorganization demonstrates that AI will drive all of Meta’s future products.  

On January 14, Meta laid off more than 1000 employees from its Reality Labs division. This is about 10% of its workforce. The layoff is part of a shift away from VR and metaverse projects. Meta will now focus on AI, Power Variables, and several VR game studios will be closed immediately as a result. The company is making these changes to address Reality Labs’ losses, which have topped $70B since 2021.  

On January 14, Meta announced a major shift in direction, laying off over 1,000 employees in its Reality Labs division as it moves away from its Metaverse focus. The cutback signals a clear transition within the company from Metaverse to AI-powered wearables and mobile technology initiatives.  

Workforce Reduction Details 

The layoffs affect about 10% of Reality Labs’ staff, with a spokesperson stating they are part of a broader plan to re-evaluate resources.  

This is part of that effort. We plan to reinvest the savings to support the growth of wearables this year. The spokesperson told Bloomberg the company had previously indicated this tactical shift in December.  

Strategic Change From VR To Wearables 

This represents a significant change from Meta’s previous Metaverse focus, which began during the pandemic and led to the rebranding from Facebook to Meta in October 2021. Reality Labs has since lost more than $70B.  

In an internal memo, CTO Andrew Bosworth said Meta wants to be more eco-friendly by moving its Metaverse investments towards mobile devices and cutting back on virtual reality spending. In December, the company had already announced plans to focus more on wearables than on the Metaverse.  

Gaming Operations Severely Impacted 

The restructuring has hit. Meta gaming plans are leading to the immediate shutdown of several VR gaming studios. The studios closed are Armichag, Sanzuru, and Twisted Pixel.  

The VR fitness app Supernatural will keep supporting its current features. However, new content and updates are on hold. Despite the closure, Tamara Sciamanna, director of Oculus Studio, stressed in an internal memo that gaming is still important to the company.  

Gaming remains the cornerstone of our ecosystem. With this change, we are shifting our investment to focus on our third-party developers and partners. This will guarantee long-term sustainability, she reportedly wrote.  

Previous Personnel Changes 

This is the latest round of job cuts at Reality Labs in April 2025. Meta also laid off staff working on the VR fitness game Supernatural. It did not share how many people were affected.  

This restructuring marks Reality Labs’ biggest step back from Metaverse projects. Meta is reallocating the Divisions’ resources as it pursues opportunities in the AI wearables market while seeking greater economic stability. 

Source: Meta Platforms Cuts Over 1,000 Reality Labs Jobs as Company Pivots from VR to AI Wearables

Quickly deploy a pre-configured OpenClaw instance with one click using an Amazon Lightsail Blueprint. Available in the US and other AWS regions, OpenClaw is an open-source, self-hosted AI agent for private use.  

How To Deploy With One Click 

Follow these steps in the AWS Lightsail console to set up OpenClaw.  

1. Log in to your Amazon Lightsail account.  

2. Go to Instances and select Create instance.  

3. Choose your region and availability zone (e.g., US East or West)  

4. Select the Linux/Unix platform.  

5. Select the OpenClaw Blueprint option.  

6. Pick your instance plan. Choose 4GB memory if available.  

7. Name your instance. Create an instance to launch.  

Key Features 

• Deploy privately to control your data and privacy.  

• The Lightsail instance has built-in security sandboxing for isolation and easy HTTPS access.  

• Default integration with Amazon Bedrock provides access via a post-deployment script in AWS CloudShell.  

• Recommended security practices, including: keeping the OpenClaw gateway closed to the Internet, generating your own SSH keys, applying patches promptly, and regularly rotating authentication tokens  

• Connect your AI assistant to Telegram, WhatsApp, and Discord.  

We are excited to share that OpenClaw is now available on Amazon LightSail. You can quickly launch an OpenClaw instance and connect it to your browser using AI features, and even link messaging channels if you want. Each LightSail OpenClaw instance comes ready with Amazon RedRock as the default AI model provider. After setup, you can start chatting with your assistant immediately. No extra configuration is needed.  

OpenClaw is an open-source, self-hosted AI agent that serves as your personal assistant on your own computer. Access OpenClaw in your browser to connect with messaging apps like WhatsApp, Discord, or Telegram. It manages emails, browses the web, organizes files, and more, not just answering questions.  

Many AWS customers have asked about running OpenClaw on AWS. Some have even shared blog posts about setting it up on Amazon EC2 instances. From my own experience installing OpenClaw on my home device, I found the process challenging and encountered security issues, including the need for strong SSH key management, secure network configuration to limit exposure, and promptly applying software security updates to protect sensitive data.  

Set up OpenClaw on your cloud easily and securely with Amazon Lightsail.  

Each Lightsail OpenClaw instance includes pre-configured security features such as session sandboxing.  

• device pairing for approved device access  

• automatic configuration backups  

You can securely access the dashboard in your browser with a single click. By default, OpenClaw uses Amazon Bedrock, but you can switch models or connect to Slack, Telegram, WhatsApp, or Discord.  

Amazon Lightsail is available in 15 AWS regions worldwide. Regions include:  

• US East (Northern Virginia)  

• US West (Oregon)  

• Europe (London)  

• Asia-Pacific (Tokyo & Jakarta)  

See Amazon Lightsail Documentation for the full list of regions. Go to the Lightsail console for pricing and more info. Check Lightsail pricing and Quick Start Documentation. 

Source: Amazon Lightsail now offers OpenClaw, a private self-hosted AI assistant 

Get started with OpenClaw on Lightsail

Microsoft will retire the managed Nginx Ingress with Application Routing Add-on for Azure Kubernetes Service on November 30, 2026, after the Kubernetes community’s decision to end support. The open-source Ingress Nginx controller support ends in March 2026.  

Key Dates 

• March 2026: The Community Ingress NGINX project will be retired and no longer receive updates or security fixes.  

• On November 30, 2026, Microsoft will end support for the NGINX Ingress Controller in Application Routing. Until then, only critical security patches will be provided.  

Migration to Gateway API 

Migrate to Kubernetes Gateway API alternatives. Gateway API provides stronger, more flexible L4 and L7 traffic management than the Ingress API.  

Microsoft offers several supported alternatives and migration options:  

• Application Gateway for Containers is a managed Layer 7 load balancer for containers. It supports Gateway API and Ingress API.  

• Microsoft is developing a new gateway API-based application for routing add-on.  

• If using a service mesh, consider the Istio add-on.  

Action Plan 

• Assess your progress: Check whether your AKS clusters use the community-maintained NGINX Ingress Controller or the NGINX Application Routing Add-on. Read the Microsoft LAM documentation to understand your migration paths to a supported platform.  

• Develop a migration timeline. Allocate resources and test the new solution in a non-production environment. Document changes and notify stakeholders. Migrate early to avoid security risks.  

Kubernetes SIG Network and the Security Response Committee are announcing that Ingress Nginx will be retired to help keep the ecosystem safe and secure. We will provide best effort maintenance until March 2026. After that, there will be no more releases, bug fixes, or security updates. Existing Ingress Nginx deployments will continue to work, and the installation files will remain available.  

We strongly recommend that users begin migration to alternatives as soon as possible to ensure continued security and support. Gateway API is the modern replacement for Ingress and is a good option to consider. If you need to keep using Ingress, you can find other Ingress controllers listed in the Kubernetes documentation. Read on for more details about Ingress, NGINX’s history, current status, and next steps.  

About Ingress NGINX 

Ingress directs network traffic to Kubernetes workloads. Gateway API now handles similar tasks. Using Ingress requires a controller. Choose among controllers by user and cloud compatibility.  

Ingress NGINX, created early in the Kubernetes project, became popular for its flexibility and features and is widely deployed across Kubernetes platforms.  

History and Challenges 

The wide range of features in Ingress NGINX has made it hard to maintain. As expectations for cloud-native software have changed, some features that were once helpful are now seen as security risks. For example, allowing users to add any NGINX configuration via snippets and annotations is now considered a serious flaw. What was once flexible has become technical debt too difficult to manage.  

Not enough maintainers supported Ingress NGINX. One or two people kept it running, mostly in their free time. Last year, maintainers announced plans to shut down Ingress NGINX and build a replacement for the Gateway API. The announcement did not attract new contributors. IN-GATE, the planned replacement, is also being retired.  

Current State and Following Steps 

Right now, Ingress NGINX is only receiving best-effort maintenance. SIG Network and the Security Response Committee have tried everything to secure additional support and keep Ingress NGINX running. To keep users safe, we have decided to retire the project.  

In March 2026, we will stop maintaining Ingress Nginx and retire the project. After that, there will be no more releases, bug fixes, or security updates. The GitHub repositories will become read-only but will stay available for reference.  

Current Ingress Nginx deployments will continue to work. Project files, such as Helm charts and container images, will still be available.  

To check if you are using Ingress NGINX, run `kubectl get pods -all-namespaces -l app.kubernetes.io/name=ingress-nginx as a cluster administrator.  

We want to thank the Ingress Nginx maintenance team for their hard work and dedication to this project. This Ingress controller has handled billions of requests across data centers and home labs worldwide. Kubernetes would not be where it is today without Ingress Nginx, and we thank the many years of effort that went into it.  

The Security Response Committee urges all Ingress Nginx users to migrate to Gateway API or another controller now. Review alternatives in the Kubernetes docs or through your vendors. 

Source: Ingress NGINX Retirement: What You Need to Know 

Google Cloud is rolling out new features in Semantic Search and AI-driven data tools. Vertex AI Search supports multi-modal search, and Vertex AI Agent Builder now offers better governance when used with Firestone and Vertex AI Vector Search. These tools enable advanced context-aware queries for auditing and content validation.  

Key Components and Capabilities 

• Vertex AI search and conversation let you build multi-modal semantic search and AI-driven chat agents. These help examine complex data in a complex control auditing setup.  

• Vertex AI agent builder now has improved governance features, making AI-powered auditing applications more secure and easier to control.  

• Firestone is a NoSQL document database for storing and syncing data. It can work with Vertex AI Vector Search to support advanced semantic queries.  

• Bringing these capabilities together unlocks new automation opportunities: quality control checks and information accuracy can now be carried out across both structured and unstructured data.  

• BigQuery and Cloud Dataflow support live data replication and processing, which is key to keeping records current and easy to audit.  

All of these technologies in the Google Cloud ecosystem enable the development of advanced AI tools for editing and quality control.  

We’re excited to share that the Vertex AI Agent Builder now includes advanced governance features enabled by the Cloud API Registry. With this update, administrators can manage which tools are available to developers right from the Agent Builder console. Developers can also use tools managed by the registry through the new API registry.  

• Following last month’s expansion of our Agent Builder Platform, we are introducing tools that accelerate every stage of the Agent Life Cycle with new ADK tools and enhanced visual features. Developers can build agents more quickly and with greater flexibility, and expanded agent engine services. Simplified scaling, while new session and memory support ensure smoother, more reliable agent interactions. These improvements help speed development and reduce operational hurdles. See below for more details.  

Together, these enhancements make Vertex AI Agent Builder a single platform for managing the full agent life cycle, making it easier to move from prototype to production. To learn more about the new features, check out the latest documentation and release notes.  

Expanding beyond data tools, Gle-tenant Cloud HSM is now generally available. This standards-compliant, highly available, and scalable HSM cluster gives you full control over your cryptographic keys and sensitive cloud work tools and general applications.  

Have full control over your cryptographic keys and can manage admin credentials using our Google Cloud APIs. Each customer receives a dedicated cryptographically isolated HSM cluster.  

In addition to these security advancements, Security Command Center (SCC) premium pay-as-you-go customers now have access to advanced AI data and compliance security features. These tools, previously available only to enterprise and premium subscribers, include: the AI security dashboard  

• data security posture management (DSPM)  

• compliance manager  

• security graph with graph search and correlated threats  

Integrating these updates, you can now manage new risks from general to AI and autonomous agents by providing integrated, automated protection for all your Google Cloud workloads. You can start a 30-day free trial to try the full SCC premium experience. 

Source: What’s new with Google Cloud – 2025 

NVIDIA GTC 2026, scheduled for March 16-19 in San Jose, will shift focus from GPU power to full rack-scale AI systems, spotlighting Blackwell Ultra architectures for agentic AI and throughput inference.  

Here are the main points about Blackwell Ultra Low-Power Optical Networking and Telco Reasoning Models for GTC 2026.  

Blackwell Ultra and Network Innovations 

• The system-focused AI column at GTC 2026 will highlight the move from counting individual cards to using rack-scale setups like NVL72 and NVL144, as well as the new NVL576, which will feature an orthogonal backplane design.  

• Blackwell Ultra (GB300) capabilities: major cloud developers use these systems for low-latency, long-running tasks. They use NVLinkswitch for scaling and NVFP4 precision for efficient inference.  

• LPO and Photonics debut as electrical internal interconnects hit their limits. NVIDIA is investing in optical connections, such as CPU and Photonics, for AI factories. Lumentum and Coherent are providing advanced CPUs to meet the high bandwidth demands of future AI systems.  

U.S. Telco Reasoning Models and Agentic AI 

• Agentic AI in Telco: NVIDIA is going beyond basic network automation and working on autonomous networks with telco reasoning models.  

• Tool-Calling Agents: These models enable AI agents to understand incidents, search databases, and take corrective actions in a controlled, trackable way, replacing old, hand-coded runbooks.  

• Industry partnerships: Telecom providers are working with NVIDIA to build 6G on Open Secure AI-based platforms. They are also using NVIDIA NEMO to fine-tune models for network operations center workflows.  

GTC 2026 Highlights 

• Keynote & Focus: CEO Jensen Huang will give the keynote on March 16, discussing the new AI Initiative software-defined infrastructure.  

• Key themes: The event will feature Vera Rubin for Agentic AI, Rubin CPX for rapid-throughput inference, and a new AI-native storage system called ICMS.  

• Sessions: Many sessions will focus on AI RAN, which brings AI to the edge of telecom networks.  

The 2026 GTC event marks a move toward treating inference as a regular operating cost, with attention on metrics such as time to first token, tokens per second, and energy efficiency.  

Telecommunications are quickly shifting toward autonomous networks, with 65% of operators viewing AI as essential for automation, according to the latest NVIDIA State of AI in Telecommunications report. Half also rank autonomous networks as the leading AI use case for return on investment.  

However, many telecom companies still lack enough AI and data science expertise. This gap makes it hard to safely scale closed-loop automation across complex networks.  

Most telecom NOCs use reactive alarm-based workflows. Engineers sift through numerous incidents with various tools, compiling data from different dashboards before resolving issues. NOCs are ideal for autonomous networks because the tasks are repeatable, allowing AI to reduce resolution time and costs.  

Tech Mahindra, a global technology and consulting solutions provider, is working with NVIDIA to help close the AI skills gap. Together, they are turning autonomous network building blocks, such as open models, tools, and guides, into resources telecom developers can easily use and adapt in their own networks. This post explains how to fine-tune reasoning models with NVIDIA Nemo so they can work like NOC engineers and safely manage closed-loop self-reasoning workflows. It covers how to:  

• Create synthetic incident data that closely matches real telecom scenarios.  

• Translate/Export Procedures into Systemic Reasoning Traces using Production-Grade Reference Workflows. This step teaches the model to coordinate tools, reason about network state, and execute end-to-end fault management tasks during fine-tuning.  

This approach gives telco teams a repeatable way to build their own AI agents for network operations. These agents can handle triage, root cause analysis, and resolution for many common incidents, helping operators move closer to TM Forum level 4, highly autonomous networks, and beyond.  

Why Do Network Operations Centers Need Reasoning Models 

Traditional NOC automation is mostly rule-based and open. Traditional NOC automation relies on rules and open-world scripts that trigger onset conditions. These scripts often struggle with noisy signals, cross-domain dependencies, and a system that can take on this work pattern in a controlled, auditable way. Instead of hard-coded runbooks and point scripts, the agent uses the model to interpret incidents, decide which tools to call, and adapt its actions based on live responses.  

Main features include:  

• AI reasoning in the tool-calling column takes over manual alarm triage by leveraging NOC tools for validation, root cause analysis, and issue resolution across current systems.  

• End-to-End Automation: Manages alarm validation, root cause analysis, and resolution for various incident types, including outages, flaps, congestion, and configuration problems.  

• Noise reduction, pull-on filters, self-clearing or low-value alarms that use historical patterns, so engineers can focus on higher priorities.  

• Resolution in seconds, not hours: Cuts down the time needed to resolve common high-volume incidents from hours to just seconds, greatly lowering MTTR.  

The result is a closed-loop self-healing network. NoC agents manage routine triage and resolution, allowing engineers to focus on proactive optimization and complex problem-solving.

Source: Building Telco Reasoning Models for Autonomous Networks with NVIDIA NeMo

OpenAI’s real-time API is now generally available following its official announcement and release in August 2025. This update includes support for remote model context protocol (MCP) servers and session initiation protocol (SIP).  

Key Features of the Real-Time API Now Available 

• General availability: The Runtime API is now production-ready and open to all paid developers.  

• Remote MCP Server Support Developers can connect AI voice agents to external tools and capabilities on any MCP-compliant server. The API automatically manages tool coils, making it easier to expand an agent’s features without manual integration.  

• SIP protocol integration: With native Session Initiation Protocol (SIP) support a common standard for initiating and managing voice communication over IP networks enterprises can connect AI voice agents directly to traditional PBX (Private Branch Exchange) systems and phone networks. This supports automated call handling, appointment scheduling, and customer service in contact centers.  

• New GPT Real-time model: The API uses the advanced GPT Real-time model, offering lower latency, more natural-sounding speech, and better performance with complex instructions.  

• Multi-modal inputs. The real-time API supports audio, image, and text inputs as well as audio and text outputs. This allows for a wide range of applications.  

For comprehensive setup and usage instructions and to explore how these new capabilities can accelerate your project, visit the OpenAI documentation today.  

OpenAI has introduced support for the remote model context protocol (MCP) server, which lets models access context from external sources, and for the Session Initiation Protocol (SIP), a widely used standard for starting and managing online voice and video calls. These technologies are integrated into its GPT-real-time speech-to-text model. These updates are available through a dedicated API. They are designed to help businesses create more autonomous voice-based agents.  

Support for remote MCP (Media Control Protocol) servers in the Real-Time API is now generally available. MCP enables communication with external applications. This lets developers program voice-based agents to access external capabilities or tools. These tools are listed as MCP servers on the internet or other servers, according to Charlie Dai, VP and Principal Analyst at Forrester.  

Remote MCP servers are not listed locally where the agent or application runs.  

OpenAI said enterprises can enable MCP support in an API session by entering the URL of a remote MCP server in the session configuration.  

Once you connect, the API automatically handles the tool calls, so you don’t need to manually wire up integrations. This setup makes it easy to extend your agent with new capabilities, the company explained in a blog post.  

Dai highlighted SIP as a standard for starting and managing real-time voice calls over IP networks, enabling AI voice agents to connect with PBX systems and phone networks.  

Examples of use cases where enterprises can take advantage of SAP support in the API comprise:  

• automated call handling  

• appointment scheduling  

• multilingual support for customer services in contact centers  

Dai added.  

Image Input And Additional Capabilities 

To make the GPT real-time model more useful for voice-based tasks, OpenAI now lets users include images, like photos, screenshots, or other visuals, along with text or audio in a session.  

This functionality enables the model to analyze and respond to image content. Users can ask questions such as “What do you see?” or “Can you read the text within this image?”, according to OpenAI’s blog post.  

Analysts say the ability to upload images is an important addition that will be useful to businesses.  

This can be seen as multi-modal support, meaning the ability to process and understand multiple forms of input, such as text, images, and audio, which is a key area in the market, Dai said. He added that competitors like Google, with Project Astra, are also focusing on multimodal live assistance. Besides image input, OpenAI has improved GPT’s real-time context awareness and memory.  

OpenAI also said the updated GPT real-time model is better at following complex instructions, calling tools accurately, and producing speech that sounds more natural and expressive.  

Dai said these improvements will help businesses use the API for fast, natural voice interactions in many areas. These improve real-time medical transcription, enhance booking assistance, improve customer service for banking, insurance, and telecom, and enhance employee support. Across industries, Penn AI said businesses using the API can now choose from two new voices: Cedar and Marin.  

Microsoft OpenAI’s largest investor also announced two text-to-speech models this week. The company said these will help unlock a wide range of enterprise uses.  

Source: OpenAI adds MCP and SIP support to gpt-realtime for smarter voice-based agents

Starting April 28, 2026, you must use Xcode 26 for App Store submissions. This version introduces new platform SDKs, including iOS SDK 26 and iPadOS SDK 26, designed to improve on-device LLM performance with the Foundation Modules framework. Swift 6.2 and macOS Sequoia 15.4 or later are required. Notable AI features include on-device integration with the cloud and ChatGPT, text summarization, entity extraction, and privacy-focused local inference.  

Key Requirements and Features 

• From April 28, 2026, all apps must be built with Xcode 26 and the latest platform SDKs, including iOS SDK 26 and macOS SDK 26. This requirement aligns with new local LLM features and the updated development workflow.  

• The new APIs let you run large language models locally, reducing reliance on cloud inference.  

• Xcode 26 loads workspaces 40% faster and has better compilation caching.  

• AI-powered tools in Xcode 26 automate inline code generation to accelerate development, generate documentation to improve code clarity, and fix bugs faster. This reduces manual work, letting developers focus on higher-level tasks and boosting overall productivity.  

• You will need a Mac with Apple Silicon running macOS 15.4 or later.  

Development Workflow Changes 

• To add on-device text generation, use the Language Model Session and always check the model’s availability before processing. Profile app uses the Foundation Models framework and tracks CPU with new tools.  

• New tools help you profile the Foundation Modules framework usage and monitor CPU usage in your app.  

• Always check model availability before starting on-device processing.  

Xcode 26 comes with Swift 6.2 and SDKs for:  

• iOS 26  

• iPadOS 26  

• tvOS 26  

• watchOS 8 or later  

• macOS Tahoe 26  

• visionOS 26  

You can debug directly on devices running iOS 15 or later, tvOS 15 or later, watchOS 8 or later, and visionOS. To use Xcode 26, your Mac must run macOS Sequoia 15.6 or later. You can debug directly on devices running iOS 15 or later, tvOS 15 or later, watchOS 8 or later, and visionOS. To use Xcode 26, your Mac needs to run macOS Sequoia 15.6 or newer.  

Xcode 26 provides advanced coding intelligence tools to streamline writing code, building tests and documentation, debugging, refactoring, and navigating projects. It supports integration with ChatGPT and cloud accounts, allows the use of custom API keys with providers that implement the chat completions of API, and offers local model operations on Macs with Apple Silicon.  

• Use the Coding Assistant to interact with code using natural language, featuring context awareness and conversation history.  

• Generate documentation, explain code, preview changes, and create playgrounds within the code editor.  

• Enhanced predictive code completion runs faster and leverages deeper code context on your Mac, resulting in more accurate and efficient suggestions that help speed up the coding process.  

Also in Xcode 26 

• The #Playground macro enables interactive debugging and code exploration in the preview panel, letting you test concepts, debug in real time, and make immediate adjustments to code logic.  

• Icon Composer simplifies icon creation from a single design file, letting you adjust depth and dynamic lighting and customize default dark and mono modes, which speeds up design iteration and ensures consistency across app icons.  

• Redesigned tabs improve project navigation, with in-tab navigation and file pinning to keep key files visible. This makes it easier to quickly access important code segments, improving workflow and reducing context switching.  

• Compilation caching reduces build times, especially when switching branches or performing clean builds, helping developers iterate faster and deliver updates more quickly.  

• New Instruments Enhance App Analysis, Processor Trace Records All Function Calls, Swift UI Profiler Analyzers View Updates, Power Profiler Tracks Battery and Thermal Impact, and CPU Counters, Identify Performance Bottlenecks  

• Swift concurrency debugging monitors async functions and threads, showing clear taps and properties.  

• String catalogs help with localization via tax-safe Swift symbols, string references, auto-compute support, and on-device AI-generated explanatory comments.  

• Voice control enables accurate Swift code dictation, recognizing syntax and automatically formatting code.  

New Features 

Hang and launch diagnostics now offer trending insights marked with a flame icon, making it easier to spot and prioritize performance issues.  

A new setting now controls how function names appear in C++ frames: Plugin.cpp.display.function-name-format.  

By default, the full function name shows, but you can include parts of the signature. For more, see http://les.idb.ibm.org/use/formatting.html#function-name-formats.  

LLDB now highlights C++ function base names by default in backtraces, helping you identify core functions. 

Source: Xcode 26 Release Notes