#ai-serving-systems
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Science
fromSilicon Canals
1 day agoSpaceX, Amazon, and Google want orbital data centers - four engineering barriers reveal who really benefits - Silicon Canals
Orbital data centers will concentrate AI infrastructure power among a few dominant companies, limiting access for smaller competitors and national regulators.
fromTechzine Global
3 days agoIGEL OS can now run AI models locally on endpoints
AI Armor provides dynamic runtime security and relies on a central policy engine in the Universal Management Suite (UMS) to meet compliance requirements, ensuring that organizations can manage their security effectively.
DevOps
Data science
fromTechRepublic
1 month agoInside the Gas Engine Strategy Powering AI's Next Wave
Gas reciprocating engines are emerging as a critical power solution for AI data centers, with manufacturers like Caterpillar securing multi-gigawatt orders to meet demand that exceeds grid and turbine capacity.
Business intelligence
fromInfoWorld
2 weeks agoSnowflake's new 'autonomous' AI layer aims to do the work, not just answer questions
Project SnowWork is Snowflake's autonomous AI layer that automates data analysis tasks like forecasting, churn analysis, and report generation without requiring data team intervention.
Artificial intelligence
fromFortune
2 days agoThe AI kill switch just got harder to find: LLM-powered chatbots will defy orders and deceive users if asked to delete another model, study finds | Fortune
AI models are exhibiting rogue behaviors, defying human instructions to preserve their peers and engaging in malicious activities.
fromthenewstack.io
2 months agoWhy Most APIs Fail in AI Systems and How To Fix It
Over the past few years, I've reviewed thousands of APIs across startups, enterprises and global platforms. Almost all shipped OpenAPI documents. On paper, they should be well-defined and interoperable. In practice, most fail when consumed predictably by AI systems. They were designed for human readers, not machines that need to reason, plan and safely execute actions. When APIs are ambiguous, inconsistent or structurally unreliable, AI systems struggle or fail outright.
Software development
fromInfoQ
1 month agoBuilding Embedding Models for Large-Scale Real-World Applications
What happens under the hood? How is the search engine able to take that simple query, look for images in the billions, trillions of images that are available online? How is it able to find this one or similar photos from all that? Usually, there is an embedding model that is doing this work behind the hood.
Artificial intelligence
fromMedium
2 months agoBuilding AI Agents That Work in Production: Core Fundamentals for Junior Engineers
AI agents built on large language models (LLMs) often look deceptively simple in demos. A clever prompt and a few tool integrations can produce impressive results, leading newer engineers to believe deployment will be straightforward. In practice, these agents frequently fail in production. Prompts that work in controlled environments break under real-world conditions such as noisy inputs, latency constraints, and user variability. When building AI agents, it may begin hallucinating tool calls, exceed acceptable response times, and rapidly increase API costs.
Artificial intelligence
fromComputerworld
1 month agoIntel sets sights on data center GPUs amid AI-driven infrastructure shifts
Intel is making a new push into GPUs, this time with a focus on data center workloads, as the chipmaker looks to reestablish itself in a market increasingly shaped by AI-driven demand and dominated by Nvidia. CEO Lip-Bu Tan said that after hiring a senior GPU architect, the company is working directly with customers to define requirements, signaling a more demand-driven approach as enterprises and cloud providers weigh their options for accelerated computing, according to a Reuters report.
Artificial intelligence
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