I've been testing Higgsfield's Supercomputer for the past few days, and it genuinely caught me off guard.

You type a task in plain language. The system picks from 61 production skills, routes each sub-task to the best available model (GPT-5.5, Claude Opus, Gemini, Seedance, Veo, Kling, and more), runs them in parallel, and delivers finished assets.

I pointed it at my own X post analytics, expecting something generic.

It came back with senior-analyst-grade breakdowns: median engagement rates, hook score analysis, content pattern detection.

Properly useful output, not a summary paragraph.

A few things that really surprised me:

- It generates up to 60 (!) minutes of video from a single prompt

- Native Obsidian integration for persistent knowledge (the "LLM wiki" concept Karpathy floated recently, already shipping, and which I was building myself just recently)

- 27 platform connectors (Slack, Drive, Notion, YouTube, Frame. io, the full stack)

- Brand and identity locks persist across sessions, so your outputs stay consistent over time

- Skills actually improve with use, version-tracked and eval-tested

The whole thing runs cloud-side on GPU-colocated infrastructure, which means generations keep running even if you close the browser. Scheduled tasks just work without a local machine.