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Bolt.new (StackBlitz)
A
FlashQLA
A
Lovable
A
Claude Code
S
TaglinePrompt 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.Build a full app from a prompt. Stripe-ready.Anthropic's CLI agent. Opus-powered, operates on your repo directly.
CategoryCodingDev PlatformDesignCoding
PricingFree + $20-$200/moFree (MIT License, open-source)Free + $25-$100/moPart of Claude Pro/Max/Team plans
Best forPMs, 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.Non-devs + solopreneurs shipping MVPs.Developers who want an agent, not autocomplete. Large refactors, tests, docs.
Strengths
  • 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
  • Generates full apps + DB + auth
  • Good for non-developers
  • Ships faster than hand-coding
  • Runs locally, edits your actual files
  • Strong on large codebases with 1M context
  • Great at multi-step tasks
Weaknesses
  • 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
  • Complexity ceiling
  • Can generate brittle code
  • Terminal-based — learning curve
  • Can't be used without Claude subscription
Kai's verdictA-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.)A-tier. The strongest 'no-code' AI builder right now. Great for founder MVPs.S-tier if you live in the terminal. Different shape than Cursor — complementary, not replacement.
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