AI content generation is about feeding your AI models with semantic and contextual information. The result is a platform that can ‘understand’ what an item is, and how it should be used. AI creates content using semantic knowledge in any form of content including video, 3d, VR and more.
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Open weight (and closed) Models with character sheet inputs
Now that we have some open weight models available to us that work with character sheet inputs, here’s a test across the models I have access to, open and closed to see how they compare. An example of the 3 character sheets I used as inputs is at the end…
Reinforced Agent: Inference-Time Feedback for Tool-Calling Agents
This paper was accepted at the Fifth Workshop on Natural Language Generation, Evaluation, and Metrics at ACL 2026. Tool-calling agents are evaluated on tool selection, parameter accuracy, and scope recognition, yet LLM trajectory assessments remain inherently post-hoc. Disconnected from the active execution loop, such assessments identify errors that are usually…
State of Routing in Model Serving
By Nipun Kumar, Rajat Shah, Peter Chng Introduction This is the first blog post in a multi-part series that shares technical insights into how our ML model serving infrastructure powers several personalized experiences at scale across various domains (e.g., title recommendations, commerce). In this introductory blog post, we will dive into…
AWS Transform now automates BI migration to Amazon Quick in days
Migrating to Amazon Quick doesn’t have to mean starting from scratch. Your dashboards encode hard-won domain knowledge: calculated fields your analysts perfected, layouts your executives rely on every Monday morning, security rules tuned to your org chart. You want AI-powered insights and serverless scale, but you’re staring at hundreds of…