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TaglineSpreadsheets with AI + live integrations baked in.Meta FAIR's open-source Python library that finally bridges the gap between neuroimaging data (fMRI, EEG, spikes) and modern deep learning pipelines.Voice AI that reads + expresses emotion.Run any open-source AI model with an API call.
CategoryDataResearchVoiceDev Platform
PricingFree + $19-$89/user/moFree (MIT open source)Free tier + pay-as-you-goPay per second of compute
Best forOps teams, marketers, anyone building dashboards from multiple sources.Computational neuroscience researchers who want to train deep learning models on brain recordings without building custom data pipelines from scratch.Therapy apps, customer service, any voice agent where emotion matters.Developers using open-source models (Flux, SDXL, Whisper, etc).
Strengths
  • Pull live data from Stripe, Slack, Google Analytics, etc.
  • AI functions inside cells
  • Modern UX
  • Unified interface across fMRI, MEG, EEG, iEEG, fNIRS, EMG, and spike trains — no more siloed modality-specific tools
  • Lazy, memory-efficient loading that scales to terabyte-scale OpenNeuro datasets without RAM blowout
  • Native HuggingFace integration for embedding stimuli (text, audio, video) using models like DINOv2, CLIP, Wav2Vec, and more
  • Pydantic-based config validation catches bad BIDS paths or filter settings at init, not after hours of wasted compute
  • Scales from local laptop prototyping to SLURM clusters without rewriting infrastructure code
  • Detects + mirrors emotional tone
  • EVI (Empathic Voice Interface) feels different
  • Expressive voice output
  • Tens of thousands of models (image, video, audio, LLMs)
  • One-line API for any model
  • Cog framework for custom model deploy
Weaknesses
  • Not a full Excel replacement for heavy users
  • Integrations best on paid tiers
  • Extremely niche audience — only useful to neuro-AI researchers with Python/PyTorch chops and access to neuroimaging datasets
  • No GUI or managed cloud environment; requires local setup and familiarity with BIDS data formats
  • Still a preprint-stage release with no arXiv paper yet — API stability and long-term maintenance are unproven
  • Niche use case
  • Pricing ramps fast
  • Cold starts on less-popular models
  • Pricing gets real at scale
Kai's verdictA-tier. The most interesting spreadsheet in years. Great for ops dashboards.If you're doing neuro-AI research, this is the plumbing you've been manually building for years — finally done right by the team that actually runs these experiments at scale. Extremely narrow use case, but within that lane it looks genuinely best-in-class. (Verdict pending Phi's full review.)A-tier in its niche. The only one that actually gets emotion right.S-tier for open-source model APIs. The default in this space.
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