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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: 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.