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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HunyuanImage 3.0 (80B) running natively in ComfyUI on a single 12–24 GB GPU: text-to-image, editing and style transfer, ~30 s per image
I’ve been working on native ComfyUI support for Tencent’s HunyuanImage 3.0, the 80B mixture-of-experts image model (13B active per step). It isn’t a wrapper around Tencent’s pipeline: it uses the normal KSampler, the normal VAE Decode and ComfyUI’s own memory management, which streams the experts from system RAM so the…
Synchronous vs. Asynchronous Agent Execution: Architecture Patterns for Production
In this article, you will learn how synchronous and asynchronous execution patterns differ architecturally, and how to choose between them when deploying LLM-based agents to…
RISED: Rubrics for Agentic Multi-Environment Selection and Self-Distillation
Training a single LLM agent jointly across diverse interactive environments has attracted increasing attention as a route to generalist agents. Existing curriculum and data-selection strategies often allocate training at the environment level or prioritize local reward-based signals, without explicitly considering relationships between current rollouts across environments for prompt-group selection. Meanwhile,…
Building a context-aware AI assistant on AgentCore and OpenClaw
Off-the-shelf AI assistants answer individual questions well, but they fall short on a different axis: continuity. Ask a stateless assistant about your garden today and it has no idea that you mentioned your fast-draining raised beds three weeks ago, that you only use organic fertilizer, or that your petunias were…