# 实测Higgsfield Supercomputer：用自然语言驱动多模型并行的AI生产力平台

- 来源：Chubby♨️ (@kimmonismus)
- 发布时间：2026-05-15 02:27
- AIHOT 分数：63
- AIHOT 链接：https://aihot.virxact.com/items/cmp5ts3tz0h6dsljx7cfid4ye
- 原文链接：https://x.com/kimmonismus/status/2054991899472302339

## AI 摘要

Higgsfield的Supercomputer平台允许用户以自然语言描述任务，系统从61种生产技能中自动选取，并将子任务路由至GPT-4o、Claude Opus、Gemini及多种图像视频模型并行处理。它能生成长达60分钟的视频，原生集成Obsidian构建持久化知识库，并通过27个平台连接器连接各类工具。平台运行于云端GPU基础设施，支持品牌标识锁定和后台任务调度。其技能在使用中通过版本追踪和评估测试不断自我改进，用户可通过浏览器或Telegram直接访问，无需本地设置。

## 正文

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.

### 引用推文

> Higgsfield AI 🧩：How Supercomputer works: 1. Access via browser or Telegram. No local setup 2. Describe your task 3. Orchestrates LLMs and image/video models. 4. Analyzes videos...