Scaling seismic foundation models on AWS: Distributed training with Amazon SageMaker HyperPod and expanding context windows | Amazon Web Services - Amazon Web Services
Scaling seismic foundation models on AWS: Distributed training with Amazon SageMaker HyperPod and expanding context windows | Amazon Web Services Amazon Web Services
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Seedance 2.0 API: A Technical Guide to AI Video Generation with Pricing and Integration Examples
This post covers the Seedance 2.0 API — a unified AI video generation interface from EvoLink.ai that exposes text-to-video, image-to-video, and reference-to-video capabilities through a single consistent API. The focus here is on technical integration patterns, model selection logic, and cost modeling — the parts that matter when you’re building a real system around this. API Design: Unified Async Task Model The central design of Seedance 2.0 is a unified async task pattern across all generation modes. Rather than separate endpoints with different request and response schemas, every generation request follows the same lifecycle: POST /v1/videos/generations — submit task, receive ID immediately GET /v1/tasks/{id} — poll for status and progress Download video from result URL once status == "
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