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Dali by Lulu
The prediction MCP that helps you save the AI generation tax
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.
About Dali by Lulu on Product Hunt
“The prediction MCP that helps you save the AI generation tax”
Dali by Lulu was submitted on Product Hunt and earned 7 upvotes and 2 comments, placing #94 on the daily leaderboard. 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.
On the analytics side, Dali by Lulu competes within Software Engineering, Artificial Intelligence, GitHub and Photo & Video — topics that collectively have 559k followers on Product Hunt. The dashboard above tracks how Dali by Lulu performed against the three products that launched closest to it on the same day.
Who hunted Dali by Lulu?
Dali by Lulu was hunted by Tal Mogendorff. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Dali by Lulu including community comment highlights and product details, visit the product overview.
Tal Mogendorff