⏰ This product is scheduled for July 9th, 2026 12:01 AM PDT.

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The prediction MCP that helps you avoid the AI generation tax. Most AI generation failures are prompt failures. You can't tell the difference until after you've burned the token. Dali scores your prompt before you generate — so you never waste a credit on a bad prompt again. Every wasted generation has a real cost (a Seedance retry is ~$6) — the live dashboard tracks what the community has saved by catching bad prompts before they burned a credit.

Hey Product Hunt 👋 I built Dali because I kept doing the same thing over and over while making ad creatives for my own product: write a prompt, generate a video, watch it come back generic or just wrong, tweak the prompt, generate again, repeat. Every one of those wasted attempts on Seedance was ~$6 gone, and I had no way to know before generating whether a prompt was actually going to work. The real unlock was realizing prompt quality isn't generic — a great Veo 3 prompt and a great Midjourney prompt are structured completely differently (camera-first vs. prose-and-params), and no generic "prompt optimizer" I found actually knew that. So Dali scores your prompt against the specific model you're targeting, tells you what's missing in plain language, and — if it's weak — hands your LLM a rewrite brief so you still do the creative writing, Dali just tells you what's broken. It started as an internal tool for my own ad pipeline. Once it was catching my own bad prompts reliably, I opened it up: it's a hosted MCP server (connect once, scores every prompt automatically), self-hostable via pip install dali-mcp, and the scoring rubric gets smarter from community usage — every prompt scored feeds a graph of what patterns actually produce A-grade results per model. Would love to know: if you generate images/video with AI regularly, what's your current process for catching a bad prompt before you burn the credit on it? Right now most people just... don't, and find out after.