Tag: events

NVIDIA and Google Cloud Empower the Next Wave of AI Builders

Posted on by faz_business



At this year’s Google I/O conference, NVIDIA and Google Cloud are accelerating the work of more than 100,000 developers in the companies’ joint developer community, which provides curated learning paths, hands-on labs and events that help them build using the full-stack NVIDIA AI platform on Google Cloud. 

Launched at Google I/O last year, the community brings together developers, data scientists and machine learning engineers who want to sharpen their AI skills on the latest NVIDIA and Google Cloud technologies. 

New additions for the community are rolling out this year, including a learning path for using the JAX library on NVIDIA GPUs, a new NVIDIA Dynamo codelab focused on inference optimizations, as well as monthly developer livestreams. 

Over the last year, the community has become a go‑to hub for AI builders using NVIDIA‑accelerated tools for data science and machine learning. The result has been production‑ready retrieval-augmented generation applications on Google Kubernetes Engine (GKE) and instrumenting observability for agent workloads. 

These AI builders are also experimenting with new large language model research and prototyping hybrid on‑premises and cloud inference for real‑world use cases like sports analytics and enterprise data pipelines. 

Building With Google DeepMind’s Gemma, NVIDIA Nemotron and Open Frameworks

NVIDIA and Google Cloud are equipping developers with learning resources and hands-on labs that combine NVIDIA libraries, open models and tools with Google Cloud’s AI platform — so they can build optimized, production‑ready AI applications faster.

For example, developers can accelerate data science and analytics with the NVIDIA cuDF library in Google Colab Enterprise or Dataproc, or deploy multi-agent applications by combining Google DeepMind’s Gemma 4 models, NVIDIA Nemotron open models and Google Agent Development Kit with Google Cloud G4 VMs powered by NVIDIA RTX PRO 6000 Blackwell GPUs in Google Cloud Run or with spot instances. 

NVIDIA and Google Cloud work closely across open frameworks like JAX so developers can build, scale and productize JAX workloads on NVIDIA AI infrastructure on Google Cloud — from single‑GPU experiments to multi‑rack deployments — while getting strong performance and a consistent experience. 

This work extends to Google Cloud AI Hypercomputer, where the MaxText framework uses these JAX optimizations to train large models efficiently on NVIDIA GPUs.

Building on the same foundation, NVIDIA Dynamo on GKE helps developers optimize large-scale inference — including mixture-of-experts models — so they can serve AI applications more efficiently with NVIDIA accelerated infrastructure on Google Cloud.

To help developers get hands-on with these capabilities, a new learning path on running and scaling JAX on NVIDIA GPUs and a new NVIDIA Dynamo on GKE inference codelab will become available next month for members in the Google Cloud and NVIDIA developer community.

Advancing Responsible AI With Google DeepMind’s SynthID and NVIDIA Cosmos

AI agents are increasingly built from a system of AI models — combining proprietary and open source models that reason, plan and act on users’ behalf. 

Amid this shift, trust and transparency are foundational, so developers and organizations can understand how these systems work and what they generate.

NVIDIA was the first industry partner to collaborate with Google DeepMind on SynthID, an AI watermarking technology that embeds robust digital watermarks directly into AI‑generated content, which helps preserve the integrity of outputs from NVIDIA Cosmos world foundation models available on build.nvidia.com.

Cosmos models provide rich 3D perception and simulation capabilities for robots, autonomous machines and other physical AI systems, while SynthID brings content transparency to the imagery and video they rely on. 

Together, they help preserve the integrity of AI‑generated content so developers can build and deploy agentic applications more responsibly across cloud, edge and real‑world environments.

Building on a Full-Stack NVIDIA and Google Cloud Platform

This year, Google I/O is putting the spotlight on new agentic experiences and tools for developers — and NVIDIA and Google Cloud are focused on ensuring builders have the infrastructure, software and learning resources they need to make the most of them. 

For developers in the community building on NVIDIA and Google Cloud, the skills and tools they learn can scale, effortlessly taking projects from prototype to enterprise‑grade workloads. 

At Google Cloud Next, Google Cloud and NVIDIA expanded their full‑stack platform to help developers train, deploy and operationalize agents on Google Cloud. This collaboration includes work on NVIDIA Vera Rubin-powered A5X instances, Google DeepMind Gemini models and more, and is being harnessed by leading AI labs and enterprises including OpenAI, Thinking Machine Labs, Schrodinger, Salesforce, Snap and Crowdstrike. Learn more in this blog.

Join the NVIDIA and Google Cloud developer community to connect with other builders and stay up to date on new tools, developer events and programs.

VSLive! Microsoft AI Hackathon 2026: Send Your Team Home With Working Code

Posted on by faz_business


Hackathon image

If you lead a development team, you already know the pattern. You approve the travel, your developers attend a great conference, they come back energized, and then the work resumes exactly as it was. The ideas don’t survive contact with the backlog.

This July at VSLive! @ Microsoft HQ in Redmond, we’re trying to change that pattern.

We’re adding the VSLive! Microsoft AI Hackathon 2026, a focused, hands-on build event that runs alongside the conference. Your developers learn during the day, then build at night, on the Microsoft campus, with Microsoft engineers and MVPs in the room. They leave with working code, not just notes.

Why this matters for dev leads

Most of your team is being asked to ship AI features into production right now. Most of them have not had uninterrupted time to actually build with Microsoft Foundry, Azure OpenAI, GitHub Copilot, or agent-based patterns under realistic constraints. Sprint work doesn’t allow it. Brown-bag sessions don’t go deep enough. Internal POCs get deprioritized.

This is structured time, with expert mentors, focused on the exact stack your team already runs on.

If you send two or three developers together, you get a small working group that returns with shared context, a real artifact, and the start of a pattern your team can extend. That’s a much better outcome than three separate sets of session notes.

Learn during the day. Build at night.

VSLive! @ Microsoft HQ runs on the Microsoft campus, which means your team is spending the week alongside the engineers, product managers, and MVPs who build and ship these tools.

Days cover Visual Studio, C#, .NET, Azure, Microsoft Foundry, Azure OpenAI, GitHub Copilot, agent-based development, and modern application patterns.

Evenings shift to the VSLive! Microsoft AI Hackathon, where the focus is building. Your developers take what they saw in sessions and apply it the same day, while it’s still fresh, with mentors on hand to unblock them.

They’ll work through the decisions that matter in production: architecture, security, user experience, and whether a pattern is actually viable for the kind of software your team supports.

The judging criteria reflect real engineering

Projects are evaluated on:

  • Architecture and design
  • Security and safety
  • Relevance to real business problems
  • User experience and execution
  • Practical use of Microsoft AI technologies

This is the right bar. AI is moving fast, but enterprise teams still have to ship software that is secure, maintainable, and defensible in a code review. The criteria reward the kind of thinking you want your developers practicing.

What your team can build

The goal is something a developer can demo, explain, defend, and improve. Not the flashiest demo, the most useful one.

That could be a C# application, a .NET service, an internal developer tool, an agent-based workflow, a line-of-business app, or a creative project applying AI to content or interactive scenarios.

Participants declare a primary category, with the option to add a secondary:

  • Microsoft .NET Powered Business Applications
  • Best AI Agent or Workflow Automation
  • Best Azure OpenAI / LLM-Powered App
  • Best GitHub Copilot Integration
  • Creative Applications

Participants retain full ownership and rights to their project IP. What your team builds belongs to them, and to you.

Who this fits

This is a fit for C# and .NET developers building business apps, web apps, desktop apps, cloud services, backend systems, and internal tools. It’s also a fit for developers exploring how AI fits inside the software they already build, whether that’s adding intelligence to an existing application, building an agent workflow, or improving a developer tool.

It’s approachable for developers new to hackathons and substantive enough for senior developers, architects, and dev leads who want practical patterns to bring back.

Your team can compete solo or as a team of up to four. Developers attending alone can form teams onsite.

Event details

Location:
Microsoft Commons Mixer, Building 98
Microsoft Headquarters, Redmond, WA

Schedule:

  • Tuesday, July 28, 2026, 6:00 PM to 10:00 PM
    • Kickoff, team formation, idea pitches, planning, first coding sprint.
  • Wednesday, July 29, 2026, 5:00 PM to 9:00 PM
    • Build time, mentor check-ins, final submissions, demo video submissions.
  • Thursday, July 30, 2026, 11:00 AM to 11:30 AM
    • Awards and select demos before the Thursday keynote.

Confirmed judges and proctors include Brian Randell, Phil Japikse, Eric Boyd, Allen Conway, and Microsoft representatives.

Prizes

Awards total up to $25,000, with a $6,000 Hackathon Grand Champion prize and additional team, solo, and category awards. Each project or team may win one monetary prize. Additional sponsored awards may be announced closer to the event.

A few logistics worth knowing

This is in-person only. There is no virtual option. Participation is capped, and once it’s full, it’s full.

If your developers are attending VSLive! @ Microsoft HQ, they can add hackathon participation during registration. There’s also a hackathon-only option for community attendees who aren’t doing the full conference.

If you’re local to Redmond, come spend an evening with us

If you’re in the Puget Sound area and the full conference isn’t in the cards, the hackathon-only pass exists for exactly this reason.

You don’t need a travel budget. You don’t need a hotel. You need an evening or two, a laptop, and an interest in building something real with the people who build the tools.

Being on the Microsoft campus after hours, working through a build with engineers, MVPs, and other developers in the room, is a different kind of experience than reading docs at your desk. The conversations are better. The unblocks are faster. The work sticks.

If you’ve been meaning to get more hands-on with Microsoft Foundry, GitHub Copilot, or agent-based development, this is a low-friction way to do it. Grab the hackathon-only pass, show up Tuesday night, and see where the build takes you.

The case for sending more than one

The single best decision a dev lead can make about this event is to send people in pairs or small groups. Two developers from the same team, in the same sessions, building together at night, will return with a shared frame of reference and the start of something your org can actually use. One developer returning alone has to re-explain everything to skeptical teammates, and most of what they learned will quietly evaporate.

If you’ve been waiting for the right reason to get a few of your developers to Redmond, this is it. They’ll learn from the people building the tools, build alongside the community, and come back with working code your team can keep improving.

And…. if you have an active Visual Studio Pro or Enterprise Subscription, don’t forget to login to my.visualstudio.com for your exclusive conference discount code.

Explore the Hackathon

Build, compete, and win.

NVIDIA and ServiceNow Partner on New Autonomous AI Agents for Enterprises

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Enterprise AI has learned to generate. It has learned to reason. Now companies are asking the next question: How should AI act?

Early agent systems have shown what’s possible, moving beyond simple prompts to take on more complex tasks. The next step is bringing those capabilities into enterprise environments — where agents must operate with context, control and consistency across real workflows.

At ServiceNow Knowledge 2026, NVIDIA founder and CEO Jensen Huang joined ServiceNow chairman and CEO Bill McDermott during the opening keynote to discuss the next phase of enterprise AI. 

The companies are expanding their collaboration across the full stack, delivering specialized autonomous AI agents that are safe and easy to adopt — powered by NVIDIA accelerated computing, open models, domain-specific skills and secure agent execution software, and bringing together enterprise workflow context from ServiceNow Action Fabric and governance from ServiceNow AI Control Tower.

ServiceNow is introducing Project Arc, a long-running, self-evolving autonomous desktop agent designed for knowledge workers, including developers, IT teams and administrators. 

Unlike standalone AI agents, Project Arc connects natively to the ServiceNow AI Platform through ServiceNow Action Fabric to bring governance, auditability and workflow intelligence to every action the autonomous desktop agent takes. It can access the local file systems, terminals and applications installed on a machine to complete complex, multistep tasks that traditional automation can’t handle, but with the controls enterprises actually need to deploy AI at scale.

The work is designed based on three requirements every company will need for long-running, autonomous agents: open models and domain-specific skills that can be customized and security that helps agents act without exposing sensitive data or systems — all running on AI factories that deliver efficient tokenomics.

Bringing this level of autonomy to enterprises requires control from the start.

Project Arc uses NVIDIA OpenShell, an open source secure runtime for developing and deploying autonomous agents in sandboxed, policy-governed environments. ServiceNow is building on and contributing to OpenShell to advance a common foundation for secure, enterprise-grade agent execution. With OpenShell, enterprises can define what an agent can see, which tools it can use and how each action is contained. 

“Project Arc represents the next step in our ongoing collaboration with NVIDIA, bringing autonomous execution to the desktop,” said Jon Sigler, executive vice president and general manager of AI Platform at ServiceNow. “By combining OpenShell’s runtime layer with ServiceNow AI Control Tower, and powered by ServiceNow Action Fabric, we’re delivering the governance and security that enterprise AI requires.” 

Open Models and Agent Skills Scale Enterprise AI

To be effective, enterprise AI systems must be adaptable. NVIDIA and ServiceNow are building on an open ecosystem that allows organizations to tailor models and applications to their specific domains and data.

NVIDIA agent skills enable specialized agents, such as ServiceNow AI Specialists, to deliver targeted capabilities across enterprise workflows. For example, the NVIDIA AI-Q Blueprint for building specialized deep research agents empowers ServiceNow AI Specialists to gather context, synthesize information and support more complex decision-making across business functions. 

In addition, the NVIDIA Agent Toolkit, including NVIDIA Nemotron open models, provide flexible building blocks and specialized skills for developing customized AI applications. To support real-world performance that these systems can perform reliably, the companies are also advancing NOWAI-Bench, an open benchmarking suite for enterprise AI agents, integrated with the NVIDIA NeMo Gym library. NOWAI-Bench includes EnterpriseOps-Gym, one of the industry’s most challenging enterprise agent benchmarks, where Nemotron 3 Super currently ranks No. 1 among open source models.

Unlike general benchmarks, these evaluations focus on multistep workflows — where enterprise AI systems often encounter real challenges — helping teams build agents that perform reliably in production environments.

Efficient AI Factories

As AI agents become long running and always on, scaling them across millions of workflows requires not just capability but efficiency — making token economics central to enterprise AI.

NVIDIA AI factories are built to deliver the lowest-cost, most-efficient tokenomics for production AI. The NVIDIA Blackwell platform delivers more than 50x greater token output per watt than NVIDIA Hopper, resulting in nearly 35x lower cost per million tokens. For enterprises running agents across millions of workflows, that efficiency can determine how quickly AI moves from pilots to broad production use.

ServiceNow AI Control Tower integrates with the NVIDIA Enterprise AI Factory validated design, extending governance and observability to large-scale AI workloads. With added agent observability capabilities, organizations can monitor behavior in real time and manage AI systems across their full lifecycle — from deployment to optimization.

AI is becoming a new way that work gets done. What’s changing now is that the core pieces required to deploy it at scale — capable agents, built-in guardrails and proven performance — are all coming together.

The companies that move fastest will be the ones that give agents the infrastructure to act, the context to make decisions and the governance to keep every action accountable — and NVIDIA and ServiceNow are making this a reality for the world’s enterprises.

Learn more about NVIDIA OpenShell and the NVIDIA AI-Q Blueprint. 

NVIDIA and Partners Show That Software-Defined AI-RAN Is the Next Wireless Generation

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AI-RAN is moving from lab to field, showing that a software-defined approach is the only viable way to build future AI-native wireless networks.

Ahead of Mobile World Congress (MWC), running March 2-5 in Barcelona, NVIDIA and Nokia announced new AI-RAN collaborations with top telecom operators across Europe, Asia and North America, powered by NVIDIA AI-RAN platforms. Industry pioneers T-Mobile U.S., SoftBank and Indosat Ooredoo Hutchison (IOH) passed implementation milestones, taking NVIDIA-powered AI-RAN outdoors and over the air.

New benchmarking results from partners like SynaXG showed that AI-RAN running on NVIDIA platforms delivers high-speed, carrier-grade performance — meaning extreme reliability — across multiple 5G spectrum bands. And over 20 AI-RAN Alliance demos built on NVIDIA platforms will be showcased at MWC, highlighting how AI is boosting 5G performance and efficiency, and unlocking new edge AI applications.

All of this represents momentum and convergence toward a common, software-defined foundation that will set the stage for secure, open and AI-native 6G systems.

AI-RAN Goes From Lab to Live

Top telecom operators and partners are using NVIDIA platforms to bring AI-RAN to commercial deployment. 

T-Mobile U.S. demonstrated concurrent AI and RAN processing on NVIDIA AI-RAN platform using Nokia’s CUDA-accelerated RAN software. In T-Mobile’s over-the-air field environment, Nokia’s AirScale massive multiple-input and multiple-output (MIMO) radio in the 3.7GHz band supported commercial devices running applications like video streaming, generative AI and AI-powered video captioning, alongside 5G. 

SoftBank’s AITRAS live field trial achieved an industry-first, 16-layer massive MIMO using fully software-defined 5G running on NVIDIA’s AI-RAN platform, marking an important technical milestone toward AI-RAN commercialization. 

IOH has implemented software-defined 5G with Nokia’s vRAN software on NVIDIA AI-RAN platforms, moving from proof of concept to pre-commercial field validation. This milestone was showcased at MWC through Southeast Asia’s first AI-powered 5G call, where AI and network intelligence operated seamlessly to enable secure, real-time cross-border connectivity, including responsive remote control of a robotic dog over the live 5G network. This achievement demonstrates IOH’s readiness to scale AI-native network capabilities and bring intelligent connectivity to communities across Indonesia.

SynaXG demonstrated fully software-defined AI-RAN using NVIDIA AI Aerial — a suite of accelerated computing platforms, software libraries and tools to build, train, simulate and deploy AI-native wireless networks — running 4G, 5G in both sub-6GHz [FR1] and millimeter wave [FR2] spectrum bands, alongside agentic AI workloads, on a single NVIDIA GH200 server. This marks the world’s first implementation of AI-RAN on FR2 bands.

SynaXG’s setup activated 20 component carriers with both a centralized unit (CU) and distributed unit (DU) on one platform, achieving a throughput of 36 Gbps and under 10 milliseconds latency. These breakthrough results highlight AI-RAN-based 5G performance as well as seamless orchestration between AI and RAN workloads.

Tripled Pace of AI-RAN Innovation

This year’s MWC will see triple the number of AI-RAN innovations over last year, with 26 out of 33 AI-RAN Alliance demos built using NVIDIA AI Aerial and a software-defined architecture.

Some of these demos include:

  • DeepSig is reinventing how devices “speak” to networks by letting AI learn a smarter signal format at both ends of the link — the communications channel that connects two devices. An AI‑native air interface jointly learns how to best encode and decode signals using neural techniques at the device and base station, removing pilot overheads and adapting to site‑specific channels. Early results on NVIDIA platforms show up to about 2x higher throughput and better spectral and energy efficiency from the same spectrum.
  • SUTD, NVIDIA and partners will show how robots and autonomous vehicles can distribute their “thinking” across the device, edge and cloud — bringing split-inferencing from concept to implementation. By deciding in real time where each AI task runs, the demos prove how AI-RAN can meet tight latency, privacy and coverage service-level agreements to scale physical AI and vision language models through the network edge.
  • zTouch Networks and partners built an AI-RAN orchestration blueprint showing how operators can safely share GPUs across AI and RAN workloads. By using NVIDIA Multi-Instance GPU technology, the blueprint steers resources in real time, maximizing GPU utilization and improving energy management while ensuring RAN quality of service. This is a key step for making multi-tenant AI-RAN solutions ready for commercial use, so operators can turn GPU capacity into revenue.
  • Northeastern University and SoftBank will demonstrate an AI switching solution for NVIDIA AI Aerial that flips seamlessly and without data loss between AI and classic algorithms for channel estimation. This selects, in real time, the best possible processing solution at all times depending on conditions, improving stability and throughput while proving AI can coexist with classical approaches.

“AI-RAN is emerging as a unifying architecture for future radio networks,” said Alex Choi, chair of the AI-RAN Alliance. “By aligning operators, vendors and researchers around software-defined, GPU-accelerated architectures, we are boosting innovation, validating new concepts quickly and building the foundation for AI-native 6G, now.”

As intelligence moves into the physical world, autonomous systems such as robots and cars depend on AI-RAN networks to see, sense, reason and act.

Capgemini is working within Project ULTIMO, a Horizon Europe-funded initiative, to show how AI-RAN can support large-scale autonomous mobility services across European cities. Autonomous shuttles equipped with the NVIDIA Jetson Orin module process sensor data locally, while select video and telemetry streams are sent over 5G to agentic AI applications on NVIDIA AI-RAN servers. These workloads handle scene understanding, incident and safety detection, and accessibility insights at scale, while mission-critical 5G gets priority access to GPU resources.

A Growing Ecosystem

A growing ecosystem of partners is forming around NVIDIA-powered AI-RAN platforms, enabling operators to choose from a range of deployment solutions. NVIDIA Aerial RAN Computer (ARC) platforms harness the NVIDIA Grace CPU and a variety of GPUs, providing a high-performance, energy-efficient compute foundation for AI-native RAN infrastructure.

  • Quanta Cloud Technology (QCT) is announcing commercial off-the-shelf AI-RAN products that support NVIDIA ARC platforms and Nokia software, giving operators standardized building blocks for AI-RAN.
  • Supermicro is extending support across the full NVIDIA AI-RAN portfolio, including NVIDIA ARC-Pro and NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, as well as ARC-Compact systems with Nokia software.
  • WNC has introduced a new AI-optimized indoor and outdoor open radio unit, integrated with NVIDIA AI Aerial Testbed and NVIDIA ARC platforms, that supports 5GA and 6G use cases.
  • Eridan has launched a 4T4R O-RU along with its 2T2R O-RU, which was integrated with NVIDIA AI Aerial, and a DU running on the NVIDIA DGX Spark desktop supercomputer, combining spectrally efficient radios with GPU-based baseband processing to create a powerful and portable outdoor base station.
  • LITEON has completed integration of its sub-6 GHz and millimeter wave radio units with NVIDIA AI Aerial, and has expanded its collaboration with ecosystem partners like Supermicro and SynaXG to accelerate AI-RAN commercialization.

Laying the Foundation for Open, Secure, AI-Native 6G

NVIDIA’s latest State of AI in Telecom report showed that the industry is stepping up AI-native RAN and 6G investments — signaling a major intercept ahead of the traditional 6G deployment cycle, with 77% of respondents anticipating a much faster time to deployment of this new AI-native wireless network architecture.

This latest progress on software-defined AI-RAN is setting the stage for secure, open and AI-native 6G systems.

NVIDIA has already open sourced NVIDIA Aerial CUDA-accelerated RAN libraries, fueling the pace of AI-RAN innovation. NVIDIA has also now joined the OCUDU (Open CU DU) Ecosystem Foundation, hosted by the Linux Foundation, contributing to open source RAN software development to accelerate research and commercialization for next-generation wireless networks.

Learn more by meeting NVIDIA and partners at Mobile World Congress. Explore key insights from the State of AI in Telecom survey.

NVIDIA Advances Autonomous Networks With Agentic AI Blueprints and Telco Reasoning Models

Posted on by faz_business



Autonomous networks — intelligent, self-managing telecommunications operations — are moving from a future vision to a current priority for telecom operators. In the latest NVIDIA State of AI in Telecommunications report, network automation emerged as the top AI use case for investment and return on investment.

Automation is different from autonomy. Beyond executing predefined workflows, autonomous networks must understand operator intent, reason over tradeoffs and decide what actions to take. Reasoning models and AI agents fine-tuned on telecom data are key to enabling this shift.

For networks to become autonomous, there’s a need for an end-to-end agentic system that includes key components like telco network models and AI agents that talk to each other and use network simulation tools to validate actions.

Ahead of Mobile World Congress Barcelona, NVIDIA unveiled an open NVIDIA Nemotron-based large telco model (LTM), a comprehensive guide for building reasoning agents for network operations, and new NVIDIA Blueprints for energy saving and network configuration with multi-agent orchestration to help operators advance toward autonomy.

And as part of GSMA’s new Open Telco AI initiative — launching tomorrow — NVIDIA is releasing the new open source LTM, implementation guide and agentic AI blueprints as open resources through GSMA, an organization for the mobile communications industry.

Open Nemotron 3 Large Telco Model Brings Reasoning to Telecom 

For telcos to successfully operationalize generative and agentic AI across their operations, AI models must have the ability to understand the language of telecom and reason through complex workflows. NVIDIA has collaborated with AdaptKey AI to release a new open source, 30-billion-parameter NVIDIA Nemotron LTM that operators around the world can use to build autonomous networks.

Built on the NVIDIA Nemotron 3 family of foundation models and fine-tuned by AdaptKey AI using open telecom datasets including industry standards and synthetic logs, the LTM is optimized to understand telecom industry terminology and reason through workflows such as fault isolation, remediation planning and change validation.

As an open model, the Nemotron LTM gives telcos full transparency into how it was trained and what data was used, enabling secure and fast on‑premises deployment within their networks, where they can build and run agents directly. It also lets telcos safely adapt and extend telecom‑tuned reasoning with their own network and operational data, so they can move toward autonomous operations without sacrificing control over data or security.

Teaching AI Agents to Reason Like Network Engineers

NVIDIA and Tech Mahindra have published an open source guide that shows telecom operators how to fine-tune domain-specific reasoning models and build agents that can safely execute network operations center (NOC) workflows.

The guide outlines a framework for teaching models to reason like NOC engineers: focus on high‑impact, high‑frequency incident categories, translate expert resolutions into step‑by‑step procedures and turn those into structured reasoning traces that capture each action, tool call, outcome and decision. These traces become the “thinking examples” the model learns from, so it understands not just what to do, but why a particular sequence of checks and fixes is safe and effective.

Using the NVIDIA NeMo-Skills pipeline, operators can fine-tune a reasoning model on these traces, laying the foundation for telco-specialized AI agents that can reason and solve problems like a network engineer.

Maximizing Energy Efficiency With New Intent-Driven Energy Saving Blueprint

Autonomous networks rely on closed‑loop operation: models that understand the network, agents that act on intent and simulation that feeds results back into the system to validate and refine decisions. The new NVIDIA Blueprint for intent-driven RAN energy efficiency brings these pieces together, helping operators systematically reduce power consumption in 5G radio access networks (RAN) while maintaining quality of service.

The blueprint integrates network test and measurement leader VIAVI’s TeraVM AI RAN Scenario Generator (AI RSG) platform to generate synthetic network data — including cell utilization, user throughput and other traffic patterns — and convert it into a simple, queryable format.

An energy planning agent then reasons over the synthetic data to generate energy-saving policies that can be simulated in AI RSG, allowing operators to safely validate energy-saving policies in a closed loop to meet their intent without changing live configurations or impacting subscribers.

Telcos Put the NVIDIA Blueprint for Network Configuration to Work

The NVIDIA Blueprint for telco network configuration is being adopted by operators around the world.

Cassava Technologies is using the blueprint to build Cassava Autonomous Network, an agentic platform designed to optimize Africa’s diverse, multi-vendor mobile network environment. The platform implements three agents: one to monitor the network and recommend configuration changes, one to apply changes with documentation and governance, and one to assess the impact of changes made and safely roll them back if they have unintended effects.

NTT DATA is implementing the blueprint to bring intelligence to traffic regulation, helping the network manage surges when users reconnect after an outage, and is deploying it with a tier 1 operator in Japan.

An AI agent looks at real-time demand across the network and then decides when and how to admit new users on specific cells. As conditions stabilize, the agent adapts its decisions, turning what used to be manual configurations into a data-driven optimization cycle for more resilient mobile networks.

Evolving Network Configuration With Multi-Agent Orchestration

To help telcos design, observe and optimize complex agentic workflows across the RAN, NVIDIA and BubbleRAN are enhancing the NVIDIA Blueprint for telco network configuration with NVIDIA NeMo Agent Toolkit (NAT) and BubbleRAN Agentic Toolkit (BAT), complementary frameworks for multi-agent orchestration.

BubbleRAN is integrating NAT and BAT into its Opti-Sphere platform to manage network monitoring, configuration and validation agents more flexibly across containers and workloads, and connect them to tools that report network metrics and traffic status so they can continuously propose and validate configuration changes.

Telenor Group will be the first telco to adopt the blueprint with BubbleRAN to enhance its 5G network for Telenor Maritime, the group’s global connectivity provider at sea.

Learn more about the latest advancements in agentic AI for telecommunications at Mobile World Congress, taking place in Barcelona from March 2-5. 

See notice regarding software product information.

The 2024 Game Awards’ biggest Game of the Year snubs and surprises

Posted on by faz_business


The Game Awards aren’t known for much in the way of shock and surprise, and so it proved with the 2024 nominees — a fairly well-rounded list in which most of the year’s best-reviewed and best-loved games got some love.

The Game Awards’ voting jury snubbed BioWare’s latest game in a series of key categories where it would have been expected to compete. It secured just one nomination, for Innovation in Accessibility, which is decided by a specialist jury.

Granted, the reception to The Veilguard has been mixed — and with its Metacritic rating settling at 82, a nomination for Game of the Year seemed beyond its reach (even though that is one point higher than Black Myth: Wukong, which did make the cut).

More tellingly, though, The Veilguard did not score nominations for Best Narrative or Best Performance, two areas where BioWare games tend to excel, and which are less review-dependent. It also missed out in Best Role-Playing Game. This was an exceptionally strong category this year: Three of the five nominees (Metaphor: ReFantazio, Final Fantasy 7 Rebirth, and Elden Ring: Shadow of the Erdtree) also secured nominations for Game of the Year, and the other two (Dragon’s Dogma 2 and Like a Dragon: Infinite Wealth) are unconventionally excellent. Even so, failing to join this company is surely not the result that BioWare or publisher EA wanted after a decade of development.

Were there any other snubs? Perhaps a few minor ones. It was a surprise not to see the much-loved EA Sports College Football 25 score a nomination in the Sports/Racing Game category, although this might be down to the broad international makeup of the jury. The Sim/Strategy Game category is missing two games with passionate fan bases and high review scores — Satisfactory and Tactical Breach Wizards — either of which might have taken the slot of the Age of Mythology remake, for example. But this was a strong category this year. Personally, I would have loved to see The Legend of Zelda: Echoes of Wisdom nominated for its fabulous music.

As ever, the intensely competitive indie categories cannot please everyone. But with 15 games nominated across Independent Game, Debut Indie Game, and Games for Impact, you have to dig down to some pretty deep cuts like Arco or 1000xResist before you find something to get upset about.

Other surprises? I don’t think anybody saw four nominations coming for Senua’s Saga: Hellblade 2, a game that has all but disappeared from the discourse since its release in May. Four nominations — including Game of the Year — for DLC, in the form of Shadow of the Erdtree, is without precedent. Black Myth: Wukong breaking through in Game of the Year despite its comparatively weak critical reputation is definitely noteworthy, as are the five nominations for one-man-band card game Balatro.

In the end, though, there’s not much in this set of nominees to ruffle any feathers — outside of BioWare’s offices, that is.

Life Is Strange: Double Exposure is more of a puzzle game than I expected

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Life Is Strange: Double Exposure simultaneously serves as a welcoming return and an exciting leap forward, as fan-favorite protagonist Max Caulfield steps back into the spotlight with new friends, a fresh mystery, and reality-bending abilities. I took the game for a spin during Gamescom and the demo revealed, to my surprise, that Double Exposure may be the series’ most mechanically intriguing entry yet.

With the game set a decade after the events of the original Life Is Strange, the now-adult Max has left Arcadia Bay and works as an artist-in-residence at Caledon University in upstate Vermont. She’s formed a new friend circle in Moses, a science enthusiast, and Safi, daughter of the university’s president. Since the cataclysmic events at Arcadia Bay, of which both endings will funnel into this narrative, Max has sworn never to use her time-rewind power again. However, her new peace becomes shattered when Safi is mysteriously murdered, prompting Max to attempt to save her by winding back the clock for the first time in years. For reasons unknown, the lengthy period of inactivity has caused Max’s power to evolve, and she manages to tear through the fabric of time and space to access an alternate timeline where Safi still lives but remains in mortal danger. Thus, Double Exposure becomes a double murder mystery with players utilizing Max’s newfound Shift power to jump between timelines to discover the identity of the killer in one reality while preventing Safi’s murder in the other.

The Gamescom demo takes place shortly after Safi’s murder. I won’t spoil the narrative details, but Max must retrieve Safi’s camera from a classroom while avoiding detection by a snooping detective. While the room is locked in her current timeline, the same may not be true in the alternate reality. Keeping track of which timeline you occupy is easy thanks to an icon in the upper-left corner labeling the reality as “Living” or “Dead,” referencing Safi’s fate in that world. Using Max’s Pulse ability, another new trick that lets her detect and reveal ghostly elements from the other timeline without doing a full swap, I find a glowing weak point between realities where switching timelines becomes possible. Making the jump sees Max pull apart the current reality like she’s opening a pair of curtains to instantaneously cross over to the other side. The snappiness of this transition makes for a cool visual.

Getting my hands on Safi’s camera becomes an involved exercise in exploring the two-story room, finding clues and hitting dead ends that can only be circumvented by switching to the other timeline. Elements such as the room’s layout, the characters’ current activities and moods, and the location of important items differ in each timeline, and the crux of puzzle-solving involves figuring out how gathering information in one world answers a question in the opposite one.

What begins as a simple search for a safe spirals into using an astronomy chart to find a vital constellation referenced by Moses, then activating a projector to overlay a star chart on a classroom mural in such a manner that the orientation of the constellation reveals the hidden location of the safe’s item. Solving this single puzzle requires several timeline shifts to unravel smaller riddles that logically build toward the solution.

Upon solving this puzzle, the detective forces his way into the classroom, triggering a stealth sequence where I need to escape the room undetected. Simply sneaking past him isn’t enough; I need a loud object to create a distraction, and it can only be found in the Living reality. Since the patrolling investigator blocks certain routes in the cluttered, box-ridden room, getting past him requires a few strategic uses of Shift, as he’s not present in the Living timeline.

While Double Exposure seems to test your noodle more than previous entries, it still heavily emphasizes managing character relationships and steering the story through dialogue choices. However, timeline hopping adds some spice to this formula. While a character may be hesitant to reveal a crucial personal secret in one timeline, their counterpart may be more forthcoming, offering information that can give Max the upper hand. Resorting to using knowledge Max technically shouldn’t possess may not go over well, though, adding a thoughtful wrinkle to conversations.

The Double Exposure Gamescom demo sold me on Shift as a fun mechanic, and I’m excited to see how the game further leverages it to tell its tale. Tack on the return of Max and I’m itching to see how this multiversal murder mystery unravels.

Marvel shows footage from Thunderbolts*, the MCU Suicide Squad, at SDCC

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When Marvel Studios first announced Thunderbolts* at 2022’s San Diego Comic-Con as part of its ambitious lineup for Phase 5 and 6 of the Marvel Cinematic Universe franchise, the movie didn’t yet have that odd asterisk in the title. It didn’t come with many details, either, apart from a July 26, 2024 release date that shifted along with many other MCU projects in the wake of the 2023 WGA strike.

In the wake of the Thunderbolts* segment of 2024’s San Diego Comic-Con, we don’t know much more! The asterisk is still a mystery: Marvel Studios President Kevin Feige said at a CinemaCon appearance, “we won’t talk more about that until after the movie comes out,” and confirmed it again at Comic-Con.

But as the core cast of Thunderbolts* took the stage, the Hall H audience was treated to a teaser in which all their characters came under fire from a mysterious foe who, according to Florence Pugh’s Yelena Belova, wants them all dead.

Traditionally in Marvel Comics, the Thunderbolts are a team-up of second-string villains or anti-heroes, though their membership and motives vary significantly depending which iteration you’re talking about. The MCU team is built of not-exactly-always-good characters introduced in previous films in the franchise: Ghost (Hannah John-Kamen, of Ant-Man and the Wasp), Red Guardian (David Harbour, Black Widow), the Winter Soldier (Sebastian Stan, the Captain America movies), U.S. Agent, aka John Walker (Wyatt Russell, Falcon and the Winter Soldier), and Taskmaster (Olga Kurylenko, Black Widow). Pugh’s Yelena, from Black Widow and Hawkeye, leads the team, with slimy mastermind Valentina Allegra de Fontaine (Julia Louis-Dreyfus, Falcon and the Winter Soldier) behind the scenes.

Who might want all those folks dead? What might those folks do to stay alive? And what the heck is that asterisk about after all? We’ll have to wait for the theatrical debut of Thunderbolts* on May 2, 2025, as the final movie in the MCU’s Phase 5.

You can find all Polygon’s coverage of SDCC 2024 news, trailers, and more here.