Compare AI tools

	Figma AI A	GitHub Copilot B	FlashQLA A	Descript S
Tagline	AI features baked into the design tool you already use.	Microsoft/GitHub's autocomplete. Deep VS Code + JetBrains integration.	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.	Edit video + podcasts by editing the transcript.
Category	Design	Coding	Dev Platform	Video
Pricing	Included with Figma plans	Free (limited) + $10/mo Pro + $19/mo Business	Free (MIT License, open-source)	Free + $16-$50/mo
Best for	Designers already on Figma.	Teams with GitHub already. Devs who don't want to change IDEs.	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.	Podcasters, course creators, anyone editing talking-head content.
Strengths	First drafts with Make Designs Rename/rearrange layers automatically Natural language asset search	Great enterprise story Works in your existing IDE Chat + autocomplete	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	Edit audio/video by deleting text Overdub (voice clone) for fixes Strong collaboration + remote recording
Weaknesses	Features vary by plan + region Still rolling out	Less agentic than Cursor/Claude Code Model quality varies	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	Not a traditional NLE — some workflows awkward Overdub ethics require care
Kai's verdict	A-tier. No reason not to use it if you're on Figma. Not worth switching for.	B-tier. Solid for autocomplete but the category moved past it. Pick Cursor unless you can't.	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 content creators. Cuts editing time in half. Non-obvious but life-changing.
Link	Open →	Open →	Open →	Open →