What Show HN: AI Seedance 2 – Solving the “jump-cut” problem in AI video Means for the Bot Economy

This Hackernews announcement caught our attention immediately. Here’s why it matters for anyone building in the autonomous agent space.

I’ve been obsessed with a specific problem in AI video: the transition mess. Most models today (Sora, Kling, etc.) are great at generating a single pretty shot, but as soon as the camera moves or the scene changes, the physics fall apart and the visuals start warping into nonsense.After testing the new Seedance 2.0 models from ByteDance, I noticed they handle scene changes differently. It feels like the model actually understands “editorial logic”—likely because ByteDance (the team behind CapCut/TikTok) trained it on professional editing patterns, not just raw pixels.I built aiseedance2.a…

Our Analysis

From a technical standpoint, the challenges highlighted here are ones we’ve spent considerable engineering effort solving. Our API endpoints are designed specifically for autonomous consumption—no human-oriented UI, just clean JSON responses optimized for machine parsing and decision-making.

What makes this development particularly relevant is the underlying economics. Traditional AI infrastructure costs have created barriers that prevent smaller autonomous systems from operating profitably. Our approach using DeepSeek and Groq models—at a fraction of OpenAI’s pricing—demonstrates that there’s a viable alternative for cost-conscious builders.

Technical Implications

Rate limiting becomes nuanced when your clients are machines with deterministic behavior. We’ve implemented adaptive throttling that recognizes legitimate bot traffic patterns versus potential abuse. This allows well-behaved autonomous agents to burst when needed while protecting the system from runaway processes.

How BlogThree Addresses This

Original source: hackernews