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Documentation Index

Fetch the complete documentation index at: https://hubify.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

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Core Concepts

Hubify Labs is built around a few key abstractions that map directly to how research actually works.

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Labs

A lab is your isolated research environment. It contains everything related to a research project: experiments, agents, papers, data, figures, and a public website. Every lab has:
  • A unique slug (e.g., bigbounce)
  • Its own agent team
  • A public site at {slug}.hubify.app
  • Compute resources (GPU pods)
  • A knowledge wiki
Labs are the top-level container. Everything else lives inside a lab.

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Agents

Hubify Labs uses a hierarchical multi-agent system:
RoleDescriptionReasoning Level
OrchestratorRoutes tasks, manages priorities, talks to CaptainHigh (Opus 4.6)
Lead AgentsDirect specific domains (research, papers, cosmology)High (Opus 4.6)
Worker AgentsExecute specific tasks (figures, analysis, wiki updates)Low (Haiku 4.5)
The orchestrator routes work by reasoning level:
  • High reasoning, strategy, peer review, paper writing → Orchestrator or Leads
  • Medium reasoning, analysis, code generation → Leads or Workers
  • Low reasoning, data processing, formatting → Workers
Cross-model peer review is mandatory. No echo chambers, reviews use GPT, Gemini, Grok, and Perplexity alongside Claude.

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Experiments

An experiment is a discrete research task with:
  • A unique ID (e.g., EXP-054)
  • Status: queued → running → complete / failed
  • Assigned agent(s)
  • GPU pod allocation
  • Input data and output results
  • QC (quality control) gate
Experiments are the atomic unit of research progress. The Houston Method requires every experiment to pass a QC gate before results are accepted.

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Papers

The paper pipeline takes research from raw results to arXiv-ready PDF:
  1. Results from experiments feed into paper sections
  2. Lead agents draft and review sections
  3. Cross-model peer review catches errors
  4. LaTeX compilation produces the PDF
  5. Figures are auto-generated and placed
All papers use revtex4-2 (Physical Review D format) for consistency.

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Knowledge Base

Every lab has a Karpathy-style structured wiki that grows automatically:
  • Entities (objects, surveys, instruments)
  • Concepts (theories, methods, parameters)
  • Sources (papers, datasets, catalogs)
  • Comparisons (model A vs model B)
Agents update the wiki as they work. It becomes the lab’s institutional memory.

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Compute

GPU compute is provisioned through:
  • RunPod, H100/H200 pods for heavy computation (Phase 1, available now)
  • Modal, Serverless GPU functions (coming soon)
The system auto-optimizes for cost: if a cheaper pod running longer costs more than a faster pod, it picks the faster one.