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FlashQLA
A
ChatGPT Operator
B
Cursor
S
Manus
S
TaglineQwen'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.OpenAI's browser agent. Clicks and types on websites for you.VS Code fork that made AI coding actually work.Autonomous AI agent that actually finishes tasks.
CategoryDev PlatformAgentsCodingAgents
PricingFree (MIT License, open-source)Included with ChatGPT Pro $200/moFree + $20/mo Pro + $40/mo BusinessFree tier + $39-$199/mo
Best forML 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.Power users willing to pay $200/mo for a browser bot.Developers. Non-developers who want to ship working code.People who want to hand off tasks entirely — trip planning, research, spreadsheet building.
Strengths
  • 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
  • Actually uses websites — fills forms, clicks, checks out
  • Built into ChatGPT
  • Good for repetitive web tasks
  • Tab completion feels like mind-reading
  • Composer for multi-file edits
  • Runs Claude, GPT, Gemini — you pick
  • General-purpose agent — research, book, build, analyze
  • Parallel task execution
  • Web browsing + file creation + coding
Weaknesses
  • 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
  • Slow vs doing it yourself
  • Breaks on complex auth flows
  • $200/mo gate
  • Can feel overwhelming for non-coders
  • Expensive at scale
  • Still hit-or-miss on complex multi-hour tasks
  • Can burn credits fast
Kai's verdictA 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.)B-tier. Still early. Manus is more flexible for less money.S-tier for coding. If you write code of any kind, this pays back the $20 in a day.S-tier in the agent category. The first one I'd give to a non-technical friend.
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