Explore the companies, models, products, research, business moves, and developer tools shaping the future of AI.
OpenAI and ServiceNow expand their partnership to bring OpenAI models and agents into ServiceNow and ServiceNow workflows into ChatGPT and Codex Reported by OpenAI on October 28, 2026.
OpenAI and ServiceNow expand their partnership to bring OpenAI models and agents into ServiceNow and ServiceNow workflows into ChatGPT and Codex.
How AI might enter the org chart—beneath, beside, and between us Reported by OpenAI on October 28, 2026.
How AI might enter the org chart—beneath, beside, and between us.
See how Legora uses OpenAI models, ChatGPT, and Codex to accelerate legal workflows, expand contract analysis, and free lawyers to focus on higher-value work Reported by OpenAI on October 28, 2026.
See how Legora uses OpenAI models, ChatGPT, and Codex to accelerate legal workflows, expand contract analysis, and free lawyers to focus on higher-value work.
NVIDIA Nemotron 3. 5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt — announced via Hugging Face on October 28, 2026 Reported by Hugging Face on October 28, 2026.
NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt — announced via Hugging Face on October 28, 2026.
If you’ve tried to get an agent into production, the model and tool use are not the blocker Reported by AWS Machine Learning on October 28, 2026.
If you’ve tried to get an agent into production, the model and tool use are not the blocker. The main bottleneck is everything around them: how many teams can build within a secure environment, which team owns each model and tool, how AgentOps and error handling stay inside a pre-defined boundary, and how platform, Workstream, and finance teams see cost and health across accounts. For a Platform team that wants to support many agentic use cases, that’s a platform product to design and operate. In this post, we refer to the team that manages the platform as the Platform team, and to the teams that build on it as Workstream teams. Workstream teams are the domain or product teams that build and own agents on the platform. Each Workstream team needs a paved road with approved models and tools, short-lived workload identity, isolated runtime and memory, automated evaluation, rollback, telemetry, and cost attribution. These controls need to remain consistent as Workstream teams customize pr…
What usually stops PyTorch training from scaling out to a cluster is the container, not the model code Reported by AWS Machine Learning on October 28, 2026.
What usually stops PyTorch training from scaling out to a cluster is the container, not the model code. Every container needs the same CUDA runtime , a PyTorch build compiled against it, NCCL (NVIDIA Collective Communications Library), the Elastic Fabric Adapter (EFA) userspace libraries, the AWS OFI NCCL plugin that connects NCCL to EFA, and a Ray version the cluster operator agrees with. Building that image and rebuilding it each time one of those pieces moves is a project of its own, and it has nothing to do with the model you set out to train. The Ray Train Deep Learning Container (DLC) is that image, built by AWS. AWS assembles the stack, tests it as one unit, publishes it in Amazon Elastic Container Registry (Amazon ECR) Public and keeps it patched. Figure 1: One EKS cluster with a control plane managed by AWS, a CPU system node (m7i.xlarge) running the KubeRay operator and Ray head, and two EFA-enabled g6.12xlarge nodes in one Availability Zone. KubeRay builds it from one RayCl…
As quality assurance (QA) automation adoption grows inside an organization, a new problem appears that has nothing to do with writing tests Reported by AWS Machine Learning on October 28, 2026.
As quality assurance (QA) automation adoption grows inside an organization, a new problem appears that has nothing to do with writing tests. A platform team stands up test automation, then a second team wants to join, then a dozen more. Soon hundreds of test cases belong to different products and squads, and everyone can see everyone else’s tests. No one can answer a basic question at a glance: is this application’s test coverage healthy right now? Test authoring stops being the bottleneck. Organizing tests and controlling who can touch them becomes the work. A second, related question follows close behind: who owns this application and its tests, and who is accountable when coverage slips? Without a clear owner, health problems have no one to land on. In the first post , we introduced QA Studio, a reference solution built with Amazon Nova Act . Teams use it to define tests in natural language and run them through AI-powered visual navigation. In the second post , we added test suites…
NVIDIA RTX Spark powers thin and light laptops built for everything that developers, creators and gamers do Reported by NVIDIA AI on October 28, 2026.
NVIDIA RTX Spark powers thin and light laptops built for everything that developers, creators and gamers do. RTX Spark laptops are now available from ASUS, Dell, HP, Lenovo, Microsoft and MSI. These powerful systems combine an NVIDIA Blackwell RTX GPU and an NVIDIA Grace CPU in a single superchip with up to 128GB of unified memory and up to 1 petaflop of FP4 AI performance. That puts the NVIDIA CUDA platform, RTX graphics and the capabilities of a personal AI supercomputer inside premium Windows laptops built to go anywhere. New and expected RTX Spark updates, coming soon, include: NVIDIA Nemotron 3.5 Transcribe, an open weight speech model for transcription, speaker attribution and translation is available on RTX Spark. The LFM3 family of open models with day-zero support. Resource management controls in NVIDIA App to allocate GPU resources between foreground apps and background agents. Phantom Blade Zero , S-GAME’s highly-anticipated action role-playing game, launches with ray traci…
Across a business, any number of AI platforms, systems, and tools may be at work simultaneously—for example, AI-powered customer platforms, conversational assistants, and workflow and process automation tools Reported by MIT Technology Review on October 28, 2026.
Across a business, any number of AI platforms, systems, and tools may be at work simultaneously—for example, AI-powered customer platforms, conversational assistants, and workflow and process automation tools. Each one is sophisticated, advanced, and quick to respond. But, when set to work, the outcomes and actions that emerge may be wrong, confused, or lacking a proper understanding of the business. Many organizations find themselves in this situation—and it could be one of key reasons they struggle to extract returns on their AI investments. DOWNLOAD THE REPORT An MIT Technology Review Insights poll finds that while every organization surveyed is using AI today to some degree, fewer than one in five (16%) are extracting transformative value from their deployments. The largest percentage of respondents (40%) say they are seeing moderate gains in specific tasks or teams, while 21% report meaningful gains, leaving 23% that are seeing minimal returns or none at all. Companies are mostly…
Pairing industrial AI autonomy and human control — announced via MIT Technology Review on October 28, 2026 Reported by MIT Technology Review on October 28, 2026.
Pairing industrial AI autonomy and human control — announced via MIT Technology Review on October 28, 2026.
