Higgsfield launches Supercomputer AI agent for tasks
Higgsfield introduced Supercomputer, a cloud-native self-learning AI agent running on an enhanced Hermes Agent framework. The system includes over 40 built-in tools and three memory layers. Users submit plain-language tasks through a browser or Telegram interface. Supercomputer routes subtasks across models such as GPT-5.5, Claude Opus, Gemini, Seedance, Veo, and Kling, then executes steps in parallel to deliver finished outputs including video and reports.
#1494Chubby♨️@KIMMONISMUSI'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.
Check it out https://higgsfield.ai/supercomputer
Chubby♨️@kimmonismusI'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.