AI will happily generate a thousand startup ideas. It can't tell you which one people will actually pay for. The Eureka Database is a library of ideas mined from real complaints on Reddit, reviews, and forums. Every idea comes with the receipts: who wants it, who's already profitable, and a working demo. Connect over MCP and your AI agent pulls the full build spec for any saved idea: the problem, the stack, the schema, even the design taste, no prompt engineering needed.
Hey Product Hunt! Jeremy here, founder of The Eureka Database.
Over a billion people visit Reddit every month, across 100,000+ active communities. A huge share of what they post is people describing problems they'd pay to fix, asking for tools that don't exist, and sharing the workarounds they've hacked together. It's the biggest pile of unfiltered demand on the internet, and almost nobody mines it.
For years my process for picking what to build was embarrassing. A shower thought, a hit of excitement, a weekend of building, then a launch to nobody. I was good at building and bad at knowing whether anyone actually wanted the thing. AI only made it worse. I could ship a polished product in a weekend, which mostly meant I could waste weekends faster.
So I built The Eureka Database.
It reads through that Reddit firehose, pulls the complaints that keep repeating, and researches each one into a real idea, with the original threads, competitors already making money, a market estimate, and a working demo.
Then it hands the whole thing to Claude Code through our MCP server. The idea, the spec, and design references from repos like Magic UI load straight into your editor, and a shared workspace keeps the build from scattering across ten tabs. It does the prompt engineering for you.
There's co-founder matching if you don't want to build alone, and an investor list for later.
Lifetime access is one payment, so please use code "PH50" for 50% off for the launch! Expires July 19th, 11:59pm EDT.
Happy to answer anything in the comments, and I'd love to hear where it misses.
— Jeremy, The Eureka Database
I have shipped over 30 projects and my problem was never building, it was picking ideas nobody asked for, so this speaks to me. One thing I keep wondering though. If hundreds of members see the same top idea with the same build spec, do we all end up shipping the same product? Any way to see how many people already pulled a spec?
This solves a real pain — I built my SaaS because a friend complained about her workflow for 6 months, not because AI suggested it.
The idea of pulling real build specs directly through MCP is genuinely useful, saves so much guesswork. One thing that would make this a no-brainer for me is a freshness or trend signal on each idea, something like how often the underlying complaint is being mentioned recently or whether the existing profitable players are gaining traction. Right now an idea could be mined from a five year old thread and feel current when it's actually fading.
"I could waste weekends faster" hit a nerve, I'm on the other side of that right now, built the product first and I'm doing the demand-validation work in reverse. One thing I'd push on: a repeated complaint proves the pain is real, but people complain about plenty of things they'd never pay to fix. Does the database distinguish "loud problem" from "monetizable problem" beyond listing competitors who charge? That gap is where most idea-mining tools miss.
Funny timing, I spent this morning digging through Reddit threads about client content chaos for exactly this reason. Though my product came from pain I lived myself, not a database. How do you tell a loud complaint from one people would pay to kill? Volume alone seems like it'd surface a lot of venting.
Half of the good indie products started as someone's Reddit complaint anyway, you just made the pipeline official. How do you filter complaints that are loud from complaints people would actually pay to fix? Congrats on the launch
would love to see a freshness score on each idea so we know how recently the underlying complaints popped up, since some pain points fizzle out fast and others keep showing up year after year
finally a tool that skips the brainstorming step and gets straight to building. i pulled a saved idea over mcp and it dropped the schema into my agent without me touching a prompt
About The Eureka Database on Product Hunt
“Turn a Reddit complaint into your next company”
The Eureka Database launched on Product Hunt on July 16th, 2026 and earned 117 upvotes and 17 comments, placing #10 on the daily leaderboard. AI will happily generate a thousand startup ideas. It can't tell you which one people will actually pay for. The Eureka Database is a library of ideas mined from real complaints on Reddit, reviews, and forums. Every idea comes with the receipts: who wants it, who's already profitable, and a working demo. Connect over MCP and your AI agent pulls the full build spec for any saved idea: the problem, the stack, the schema, even the design taste, no prompt engineering needed.
The Eureka Database was featured in Productivity (656.2k followers), Tech (628k followers) and Vercel Day (26 followers) on Product Hunt. Together, these topics include over 313.6k products, making this a competitive space to launch in.
Who hunted The Eureka Database?
The Eureka Database was hunted by Jeremy Galang. 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 The Eureka Database stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Jeremy Galang