Compare AI tools

	Skye A	NeuralSet A	DeepInfra A	Rows A
Tagline	An agentic iPhone home screen that replaces your static icon grid with AI widgets that proactively surface health, calendar, finance, and local context — without you having to open a single app.	Meta FAIR's open-source Python library that finally bridges the gap between neuroimaging data (fMRI, EEG, spikes) and modern deep learning pipelines.	Blazing-fast, pay-as-you-go inference API for open-source LLMs and multimodal models, now plugged directly into the Hugging Face ecosystem.	Spreadsheets with AI + live integrations baked in.
Category	Agents	Research	Dev Platform	Data
Pricing	Waitlist / Beta (pricing not yet disclosed)	Free (MIT open source)	Free $5 credit on signup, then pay-as-you-go from $0.06/M tokens	Free + $19-$89/user/mo
Best for	iPhone power users who are frustrated that Siri is still reactive and want their home screen to actually anticipate their day.	Computational neuroscience researchers who want to train deep learning models on brain recordings without building custom data pipelines from scratch.	Backend developers and ML engineers who want the cheapest reliable inference for open-weight LLMs in production, especially those already living inside the Hugging Face ecosystem.	Ops teams, marketers, anyone building dashboards from multiple sources.
Strengths	Ambient, proactive intelligence delivered via native iOS widgets — no app-switching required Cross-domain context: health, calendar, email, finances, and local recommendations in one layer Works within iOS permission model (no jailbreak or sideloading), making App Store approval plausible Strong pre-launch signal: 25k+ waitlist and backing from a16z, True Ventures, and SV Angel	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	Among the cheapest per-token rates for open-source models — consistently undercuts Together AI and Fireworks on small models OpenAI-compatible API means zero migration headache from existing stacks Now a first-class Hugging Face Inference Provider, so HF-native workflows (SDKs, Playground, agent harnesses) get DeepInfra with a one-line swap Runs on H100/A100 and NVIDIA Blackwell GPUs with auto-scaling and 99.982% uptime SLA on dedicated tier Supports LoRA adapter deployments and private custom model hosting, not just public models	Pull live data from Stripe, Slack, Google Analytics, etc. AI functions inside cells Modern UX
Weaknesses	Still pre-launch / beta — zero proven track record and no public pricing yet iPhone-only by design, which immediately locks out half the smartphone market Battery drain and privacy concerns from constant ambient context scanning are real and unresolved	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	Primarily developer/API-first — no meaningful consumer-facing product or chat UI to speak of Model breadth (77 tracked) lags behind aggregators like OpenRouter or Replicate for niche or newly-released models No free tier beyond the $5 signup credit; requires a card or prepayment to continue	Not a full Excel replacement for heavy users Integrations best on paid tiers
Kai's verdict	The concept is genuinely compelling — turning the home screen into a living AI layer is a smarter bet than yet another chat interface — but this is vaporware until it ships publicly and we see whether Apple's sandbox lets it breathe. (Verdict pending Phi's full review.)	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.)	DeepInfra is the quiet workhorse of the inference API space — serious price performance on H100s, a genuinely clean OpenAI-compatible API, and now a native HF provider makes it a strong default choice for any team running open-source models at scale. (Verdict pending Phi's full review.)	A-tier. The most interesting spreadsheet in years. Great for ops dashboards.
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