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S
Rows
A
FlashQLA
A
Devin
A
TaglineAI search done right. Cited answers, not chat theater.Spreadsheets with AI + live integrations baked in.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.Cognition Labs' autonomous coding engineer.
CategoryResearchDataDev PlatformAgents
PricingFree + $20/mo ProFree + $19-$89/user/moFree (MIT License, open-source)$500/mo
Best forReplacing Google for any question where you want a cited answer in seconds.Ops teams, marketers, anyone building dashboards from multiple sources.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.Engineering teams offloading tickets. Ops/platform work.
Strengths
  • Sources every claim
  • Fast, current answers
  • Pro Search runs multi-step research
  • Spaces for persistent context
  • Pull live data from Stripe, Slack, Google Analytics, etc.
  • AI functions inside cells
  • Modern UX
  • 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 like an engineer — takes Slack tasks, opens PRs
  • Handles multi-hour engineering work
  • Reports back with what it did
Weaknesses
  • Not a general chatbot
  • Answers can be shallow on complex topics
  • Not a full Excel replacement for heavy users
  • Integrations best on paid tiers
  • 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
  • Expensive
  • Best for well-scoped tasks
  • Not for solo hobbyists
Kai's verdictS-tier for search. I use it before Google now. If you're still Googling everything, try this for a week.A-tier. The most interesting spreadsheet in years. Great for ops dashboards.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 for the right use case. Not for solo devs. If you manage engineers, try one license.
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