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The Universal Agent Evaluation Framework (UAEF) is a framework-agnostic system for evaluating AI agents. UAEF converts each framework's traces into one canonical format, scores them with 36 built-in metrics across 7 dimensions, and stores results so teams can compare agent versions and catch regressions.
AI agents spend most of their time idle, waiting on a human prompt, a tool result or a model response. Kubernetes wasn’t built for that. Running one Pod per...
Introducing ClickHouse Observability: fully managed observability with the speed and scale of ClickHouse. Just send telemetry; we'll handle the rest. Plus, build the observability experience you've always wanted with a simple prompt in Observability Apps.
For several years, the conversation about enterprise artificial intelligence has focused on capability: which model is smartest and which GPU is fastest. That made sense when technology was the constraint. It isn’t anymore. AI can now do far more than most organizations will allow, and the gap is trust. That’s the core finding of “Permission […] The post Cisco research: Trust, not technology, is now the speed limit on enterprise AI appeared first on SiliconANGLE.
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Repeatable tasks like status updates, release notes, and ticket creation can be encoded in AWS Quick through agents and skills. This post covers how agents, skills, and your data fit together in AWS Quick, how to build and share skills for your team, and how the official catalog and custom agents help you use them.
Learn how Stripe built Kai, a company-wide AI agent on LangChain, LangGraph, and Deep Agents, reaching 5,000 users in roughly 4 weeks.
Enterprises are moving beyond AI pilots, putting greater emphasis on governance, production readiness and measurable business outcomes. Research from Deloitte highlights a significant gap between enterprise AI ambition and execution: 54% of organizations plan to move at least 40% of their AI initiatives into production within six months. Yet only 25% have done so today. IBM […] The post IBM builds on hybrid cloud and governance to advance its enterprise AI operating model appeared first on SiliconANGLE.
Article URL: https://www.sandromaglione.com/articles/indie-game-development-with-ai Comments URL: https://news.ycombinator.com/item?id=50432523 Points: 22 # Comments: 16
ClickHouse Security, an agentic SIEM built on RunReveal and ClickHouse Cloud, is now in private preview, with AI agents, 100+ integrations and built-in detections running on your own ClickHouse service.
One open source entry point for all AI usage in your company. See every model call, who made it and what it cost, natively traced in Langfuse.
LangSmith Bring Your Own Cloud is now generally available on AWS, giving Enterprise teams managed observability, evaluation, and deployment inside their own VPC.
Article URL: https://www.wired.com/story/meet-the-sad-wives-of-ai/ Comments URL: https://news.ycombinator.com/item?id=50438429 Points: 27 # Comments: 13
NVIDIA Nemotron 3.5 Super VL, a 120B-parameter multimodal vision-language model, is now available in Amazon SageMaker JumpStart. Deploy the NVFP4 or BF16 variant in a few steps, then run text, image, and video inference for document intelligence, computer-use agents, and multimodal agentic workloads.
Hugging Face has launched integration with the Google Cloud Marketplace, allowing organizations to link their Hugging Face accounts to Google Cloud for consolidated billing. This enables teams to manage Hugging Face usage—such as GPU workloads, model deployment, and inference services—through their existing Google Cloud billing account, eliminating the need for separate vendor onboarding or credit card payments. The integration supports all paid services, including Jobs, Inference Endpoints, and additional storage, with charges appearing on the Google Cloud invoice. Admins can subscribe via the Google Cloud Marketplace, link their organization, and await approval before usage is billed through Google Cloud. Once approved, the Google Cloud account becomes the active payment method, and usage can be monitored in both Hugging Face’s billing settings and Google Cloud’s billing reports.
Automate prompt engineering with Promptim's optimization library. Save time, improve AI performance, and switch models faster.
Autonomous vehicle (AV) developers need reasoning models that can operate within the compute and latency constraints of an onboard system. Larger models provide...
arXiv:2611.09977v1 Announce Type: new Abstract: Language models are widely used as synthetic respondents in surveys, behavioral experiments, and market research. Most of these respondents read text, whereas shoppers in a store choose by looking at shelves, which makes vision-language models (VLMs) natural synthetic shoppers. We test whether VLM shoppers follow real consumers at two levels. At the purchase level, VLM shoppers built from household records plan their trips from a store directory and select products from shelf images, and we compare their purchases with six public datasets from three countries. The synthetic baskets hit held-out purchases as often as the households' own past trips, and synthetic group differences correlate with real ones twice as strongly as those of a trained regression. Without fitting, VLM shoppers reproduce alcohol purchases by gender and age in Russia and trip sizes by store distance in Taiwan, and respond to situations, prices, and displays within measured ranges. At the level of a single shelf choice, a model's answer confounds the product with its position and letter label. We therefore introduce ShelfSight, shelf renderings that vary products, positions, labels, viewpoints, and prices independently, together with a factor model that separates product preferences from the effects of label and position, which we call answer effects. On ShelfSight, hosted VLMs read the shelf, follow stated needs, keep their product preferences across views, and respond to price as budget shoppers. These answer effects, a preference for the letter A and for positions in the image, are estimated once per image type and removed. VLMs are thus good synthetic shoppers whose corrected shelf choices reflect product preferences.
Article URL: https://github.com/Aarv-1o/Attentry Comments URL: https://news.ycombinator.com/item?id=50430050 Points: 2 # Comments: 0
Article URL: https://simplecaseconverter.com/ Comments URL: https://news.ycombinator.com/item?id=50436473 Points: 2 # Comments: 0
