Agent skills capture repeatable engineering work in files teams can review and reuse, while tools, project context, and permissions remain specific to the host that runs them. The post Build once, run on any agent: A practical guide to agent skills appeared first on The GitHub…
Build once, run on any agent: A practical guide to agent skills Cyber at the Frontier A live keynote featuring OpenAI's latest cyber announcements.
Deploy frontier AI models on AWS Trainium with SGLang-Neuron SGLang-Neuron is now publicly available through an AWS Deep Learning Container. With SGLang-Neuron, you can serve models and generate reinforcement learning rollouts with SGLang on AWS Trainium using the same SGLang interface you already know, all running on native PyTorch.
Dispelling LTAP myths Reynold Xin's post "From monolith to Lakebase to LTAP" explained what LTAP is and...
How Natera designed an agentic AI system for lab ops with Amazon Bedrock AgentCore Natera built SWARM, an agentic AI system on Amazon Bedrock AgentCore that captures institutional lab-operations knowledge and deploys it at scale. This post walks through the full architecture, from knowledge base construction to deployment and evaluation, delivering about 130…
Microsoft Research Focus – Edition #98 What's new in research at Microsoft? Host Anson Ho explores the latest, including AI-powered universal translation, a breakthrough in a 31-year-old mathematics challenge, and advances making AI coding agents faster and more efficient. The post Microsoft Research Focus – Editio…
Three Lincoln Laboratory technologies receive 2026 R&D 100 Awards A secure operating system, a quantum microsystem platform, and a 5G interference-canceling algorithm are named among the year's most significant innovations.
Introducing Claude 4 Discover Claude 4's breakthrough AI capabilities. Experience more reliable, interpretable assistance for complex tasks across work and learning.
Four Amazon Bedrock inference choices that affect cost Your Amazon Bedrock inference bill can keep climbing even after you tune prompts and application logic. Four request-level choices (cache duration, service tier, reasoning effort, and maxTokens) change how many tokens the service processes and the rate applied. We show how we …
How Amplitude ships product improvements in 1–2 days with OpenAI Built primarily on OpenAI models, Amplitude’s proactive product agent, Wave, identifies what to build, helps teams ship changes with coding agents, and measures the impact.
Tokenomics at scale: Track and cap Amazon Bedrock spend per IAM principal with CUR 2.0 and AWS Budgets Get per-identity Amazon Bedrock cost visibility and enforceable spend limits using only AWS-native features, with no application code for the common case. Attribute spend with IAM principal tags, report with CUR 2.0 or Cost Explorer, and enforce with AWS Budgets actions that a…
How Constellation is building a governed data foundation on Databricks Constellation operates the largest nuclear fleet in the United States. For them,...
Eclipse Aerospace simplifies flight-data ingestion with Databricks As organizations in aerospace, manufacturing, life sciences, and other data-intensive...
LLMON: An LLM-Native Markup Language, now in Granite 4.1 3B and 8B Optimizing cost per task and proving agent ROI on Amazon Bedrock Comparing per-token prices hides the real cost of running a model on Amazon Bedrock. This post shows how to calculate the true cost per task across four hidden cost drivers, reduce it with caching, routing, and output constraints, and prove agent ROI with human baselines, hold…
Building a self-service LLM gateway with Amazon Bedrock and LiteLLM Build a self-service LLM gateway with LiteLLM on AWS Fargate that puts Amazon Bedrock foundation models behind an OpenAI-compatible API. Developers get instant access through a single API key, while platform teams gain per-user cost controls, quota isolation, and observability.