The platform for
scientific discovery.
Thousands of datasets. Multi-agent multi-model peer review. GPU compute on demand. Make real discoveries, not summaries — in the brief window before the moats close.
in the browser
native macOS app
in your terminal
I built this to run my own research — not as a side project, but as full infrastructure for an independent scientific program. After months of running it, I realized I'd built something other researchers needed.
— Houston Golden, Hubify Labs
For the first time in history, one person can do the work of a department.
The AI tooling has crossed a threshold. Datasets are open. Compute is rentable by the hour. What's been missing is the infrastructure layer — the orchestrator that runs while you sleep, the pipeline that turns raw data into a publishable paper, the agent team that argues with itself before showing you anything.
That window is open now. It won't last indefinitely. But right now, anyone with a genuine research question has a real shot at a real discovery — with or without an institution behind them. That's what this is built for.
Everything in one place. Always in sync.
The full Discovery IDE — experiments, papers, agents, data, and compute — in a single interface that runs in your browser, desktop, and terminal simultaneously.
Thousands of datasets.
Every domain in science.
First-class connectors to HuggingFace, NASA, arXiv, Wolfram, the K-Dense 250-database catalog, and the long tail of domain-specific archives. Pull data. Train custom models. Publish enhanced catalogs back.
11 agents. 4 model providers. Zero echo chamber.
Every lab ships with a pre-wired team: 1 orchestrator, 4 domain leads, 6 workers, plus 4 cross-provider reviewers. You stay in the Captain's seat. They handle the rest — and they argue with each other before they show you anything.
Every meaningful result is adversarially reviewed by 4 agents from different providers before it reaches you. Each model family has different blind spots — so they catch what the others miss.
Spin up labs as fast as you can name them.
Each lab is a self-contained discovery environment — agents, compute, a public site, and the publishing pipeline already wired up. Run as many in parallel as your work needs.
Built for science.
Not retrofitted.
Jupyter is for notebooks. k-dense has incredible dataset coverage but no agents. feynman.is is CLI-first but has no GPU, no paper pipeline, and no memory. Hubify Labs was built from scratch for independent researchers who want real discoveries.
| Capability | Hubify Labs | k-dense.ai | feynman.is | Jupyter / Colab |
|---|---|---|---|---|
| Multi-agent orchestration | ✓ | ✗ | ✗ | ✗ |
| Cross-model review (GPT-5.4 · Gemini 3.1 · Sonnet · Sonar) | ✓ | ✗ | ✗ | ✗ |
| GPU compute integration (H200, credits) | ✓ | ✗ | ✗ | ~ |
| Publish-ready loop (paper → arXiv → HuggingFace) | ✓ | ✗ | ~ | ✗ |
| Novelty scoring | ✓ | ✗ | ✗ | ✗ |
| 4-layer agent memory | ✓ | ✗ | ✗ | ✗ |
| 250+ scientific dataset connectors | ✓ | ✓ | ~ | ✗ |
| Scientific skills catalog | ✓ | ✓ | ~ | ✗ |
| Captain-configurable public lab site | ✓ | ✗ | ✗ | ✗ |
| Web + Desktop + CLI TUI (equivalent IDEs) | ✓ | ✗ | ~ | ✗ |
| Always-on orchestrator (24/7, no babysitting) | ✓ | ✗ | ✗ | ✗ |
✓ full support · ~ partial · ✗ not supported · Based on public information as of early 2026.