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TaglineAn 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.Qwen's open-source GPU kernel library that squeezes 2–3× more speed out of linear attention on NVIDIA Hopper hardware — if you're lucky enough to own one.Spreadsheets with AI + live integrations baked in.Run any open-source AI model with an API call.
CategoryAgentsDev PlatformDataDev Platform
PricingWaitlist / Beta (pricing not yet disclosed)Free (MIT License, open-source)Free + $19-$89/user/moPay per second of compute
Best foriPhone power users who are frustrated that Siri is still reactive and want their home screen to actually anticipate their day.ML engineers and researchers running Qwen3.x linear-attention models on H100/H200 clusters who need to close the gap between theoretical GDN efficiency and actual hardware throughput.Ops teams, marketers, anyone building dashboards from multiple sources.Developers using open-source models (Flux, SDXL, Whisper, etc).
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
  • 2–3× forward-pass and ~2× backward-pass speedup over FLA Triton kernels on Hopper GPUs
  • Gate-driven automatic intra-card context parallelism boosts SM utilization in long-sequence, small-head-count regimes without manual config
  • Hardware-friendly algebraic reformulation reduces Tensor Core, CUDA Core, and SFU overhead with no numerical precision loss
  • MIT licensed and fully open-source — drop it straight into Qwen3.x training and inference pipelines
  • Pull live data from Stripe, Slack, Google Analytics, etc.
  • AI functions inside cells
  • Modern UX
  • Tens of thousands of models (image, video, audio, LLMs)
  • One-line API for any model
  • Cog framework for custom model deploy
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 narrow hardware requirement: SM90+ only (H100/H200, DGX Spark) with CUDA 12.8+ and PyTorch 2.8+ — useless outside Hopper-class clusters
  • GDN/Qwen-specific: not a drop-in replacement for FlashAttention-style softmax kernels, and won't help you if you're not running linear-attention Qwen models
  • Very new, minimal community adoption or third-party validation yet
  • Not a full Excel replacement for heavy users
  • Integrations best on paid tiers
  • Cold starts on less-popular models
  • Pricing gets real at scale
Kai's verdictThe 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.)A genuinely impressive, laser-focused kernel optimization from the Qwen team — real speedups on real hardware — but its utility is gated behind Hopper GPUs and Qwen's GDN architecture, making it a niche power tool rather than a broadly useful library. (Verdict pending Phi's full review.)A-tier. The most interesting spreadsheet in years. Great for ops dashboards.S-tier for open-source model APIs. The default in this space.
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