Multi-agent · Multi-model · 31 agents across 4 providers
14 scientific domains · One researcher at the helm
“I ran an autoencoder on 17.65 million spectra from DESI DR1. It found 195,829 objects that don’t match any known pattern. Total compute cost: $200. The data was public. The pipeline took a weekend. The question I couldn’t shake: why hasn’t anyone done this before?”
Full platform in the browser. Experiment dashboard, live agent logs, paper editor, GPU management. Works everywhere.
Native macOS. Embedded terminal, global shortcuts, offline agent runner. It feels like an IDE because it is one.
Full terminal UI. Pipe experiments from shell scripts, watch logs, manage pods from cron. The keyboard-first researcher's home.
Each provider carries different training philosophies, different blind spots, and different tendencies toward agreement. By routing review across Anthropic, OpenAI, Google, and open-source models in adversarial roles, no single model can validate its own mistakes. Your science gets reviewed by systems competing to find flaws in each other’s reasoning.
“We tell researchers what they need to hear, not what they want to hear.”
Hubify Labs · Design principleCaptain → Orchestrator → Leads → Workers. Every experiment runs a full agent hierarchy with no single points of failure.
Claude, GPT-4o, Gemini, and open-source models review each other's outputs. Consensus isn't the goal — finding flaws is.
Astrophysics, genomics, climate, materials science, particle physics, neuroscience, and more. Domain-specific preprocessing included.
Direct connectors to DESI, Gaia, SDSS, LIGO, TESS, ZTF, Chandra, arXiv, and 40+ more. The data is already there.
RunPod H200s provisioned and terminated automatically. Pay for exactly what each experiment costs. No idle infrastructure.
From experiment results to LaTeX draft. Agents write, cite, and format. You review and submit. The loop closes in hours.
Web app, desktop IDE, and CLI share a single experiment state. Start in the browser, check logs from terminal, review on desktop.
The platform accumulates knowledge from every run. Methods that worked, paths that failed, cross-experiment patterns. Nothing is lost.
The platform is in early access. Every researcher who joins now gets full access to all agents, all datasets, and all compute tooling — no credit card, no expiry. When we introduce paid plans, the philosophy will be the same as the product: transparent, no surprises, priced for independent researchers, not enterprise procurement departments. We will email you before anything changes.
Request early access| Capability | ChatGPT | Notebook LM | Co-Pilot | Hubify Labs |
|---|---|---|---|---|
| Multi-agent orchestration | ||||
| Adversarial cross-model review | ||||
| Connects to scientific datasets | ||||
| GPU compute management | ||||
| Writes LaTeX / paper drafts | ||||
| Persistent experiment memory | ||||
| Anti-sycophancy by architecture |
The data is public. The compute is rentable by the hour. The models can code, analyze, and critique at a level that would have required a full research team five years ago. The only thing missing was the platform that tied them together — that routed the right task to the right agent, kept the context alive across experiments, and refused to tell you what you wanted to hear.
We built it for one researcher, doing one real project: 17.65 million DESI spectra, one autoencoder, $200 in compute. What came out was a 195,000-object anomaly catalog that no institutional lab had produced, for any survey, at full scale. Not because we were smarter. Because we were willing to do the work that doesn’t fit anyone’s job description.
“The window is open right now. Public data, commodity GPUs, capable AI agents. This level of independent scientific leverage has never existed before and will not last long. We built the platform to help you use it.”