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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.
Curious how the scoring actually works under the hood — is it a model judging the prompt, some heuristic on structure, or both? And does it learn from which prompts the community ends up regenerating, or is the feedback loop one-way?
Curious how the scoring actually works under the hood. Is it running the prompt through a smaller model to predict output quality, or is it more of a heuristic check on the prompt structure itself?
how does it actually score the prompt though, like what is it looking for to flag it as bad before you hit generate?
The dashboard idea is genuinely useful, knowing what failed before spending credits saves real money on the pricey video models. Scored a test prompt at 62 and it did flag something I'd have missed on my own.
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 8 upvotes and 5 comments, placing #85 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.
Dali by Lulu was featured in Software Engineering (42.7k followers), Artificial Intelligence (473.1k followers), GitHub (41.3k followers) and Photo & Video (2k followers) on Product Hunt. Together, these topics include over 138.7k products, making this a competitive space to launch in.
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.
Want to see how Dali by Lulu stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Tal Mogendorff