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S
Bolt.new (StackBlitz)
A
FlashQLA
A
Fathom
S
TaglineAI search done right. Cited answers, not chat theater.Prompt to deployed full-stack app in the browser.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.Meeting notes, free forever for individuals.
CategoryResearchCodingDev PlatformMeetings
PricingFree + $20/mo ProFree + $20-$200/moFree (MIT License, open-source)Free for individuals + $15-$29/user/mo teams
Best forReplacing Google for any question where you want a cited answer in seconds.PMs, founders, non-devs shipping MVPs.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.Solo operators, freelancers, small teams on a budget.
Strengths
  • Sources every claim
  • Fast, current answers
  • Pro Search runs multi-step research
  • Spaces for persistent context
  • Full-stack generation + live preview
  • Deploy to Netlify in one click
  • Works in-browser — no install
  • 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
  • Unlimited free tier for solo use
  • Strong summaries + action items
  • Works in Zoom, Meet, Teams
Weaknesses
  • Not a general chatbot
  • Answers can be shallow on complex topics
  • Quality ceiling for complex apps
  • Can get into loops for non-trivial bugs
  • 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
  • Bot-joining model
  • Team features gated
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. Best for fast prototypes. Competitive with Lovable — try both.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.)S-tier for solo + free. The best free option, hands down.
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