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Side-by-side: what they do, what they cost, what Kai actually thinks. Pass up to 4 tools via ?tools=claude,chatgpt,gemini.
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Devin
A
Rows
A
FlashQLA
A
GitHub Copilot
B
TaglineCognition Labs' autonomous coding engineer.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.Microsoft/GitHub's autocomplete. Deep VS Code + JetBrains integration.
CategoryAgentsDataDev PlatformCoding
Pricing$500/moFree + $19-$89/user/moFree (MIT License, open-source)Free (limited) + $10/mo Pro + $19/mo Business
Best forEngineering teams offloading tickets. Ops/platform work.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.Teams with GitHub already. Devs who don't want to change IDEs.
Strengths
  • Works like an engineer — takes Slack tasks, opens PRs
  • Handles multi-hour engineering work
  • Reports back with what it did
  • 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
  • Great enterprise story
  • Works in your existing IDE
  • Chat + autocomplete
Weaknesses
  • Expensive
  • Best for well-scoped tasks
  • Not for solo hobbyists
  • 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
  • Less agentic than Cursor/Claude Code
  • Model quality varies
Kai's verdictA-tier for the right use case. Not for solo devs. If you manage engineers, try one license.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.)B-tier. Solid for autocomplete but the category moved past it. Pick Cursor unless you can't.
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