Compare AI tools

	Skye A	Perplexity S	FlashQLA A	Aider 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.	AI search done right. Cited answers, not chat theater.	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.	Terminal-based AI pair programmer. Git-aware, model-flexible.
Category	Agents	Research	Dev Platform	Coding
Pricing	Waitlist / Beta (pricing not yet disclosed)	Free + $20/mo Pro	Free (MIT License, open-source)	Free (open source) + whatever API you use
Best for	iPhone power users who are frustrated that Siri is still reactive and want their home screen to actually anticipate their day.	Replacing Google for any question where you want a cited answer in seconds.	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.	Developers who want open-source tooling with full control.
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	Sources every claim Fast, current answers Pro Search runs multi-step research Spaces for persistent context	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	Works in any terminal Auto-commits changes with meaningful messages Works with any model (Claude, GPT, local) Minimal learning curve
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	Not a general chatbot Answers can be shallow on complex topics	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	Terminal-only Less agentic than Claude Code Setup on Windows is fiddly
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.)	S-tier for search. I use it before Google now. If you're still Googling everything, try this for a week.	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 right answer if you want open-source + terminal-native + model-agnostic.
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