IdeaKiln – A platform to validate your startup ideas before building

Hacker News

Hey HN, I'm Sebastian, and I just launched IdeaKiln.com — a platform to help makers, indie hackers, and founders validate their early-stage ideas, prototypes, or MVPs through community feedback. The idea came from personal frustration. I’ve started several projects where I spent weeks — sometimes months — building only to realize no one really wanted what I made. Without a large audience or email list, it’s hard to get early signals. IdeaKiln is my way of solving that problem. Instead of silently building in isolation, IdeaKiln gives you a place to: - Share what you’re working on (even in rough form) - Get early feedback and upvotes from fellow makers - Discover whether your idea resonates before committing too much time - Pivot or refine your direction based on real responses There’s no need for a landing page or pitch-perfect copy. Just post your raw idea and start the conversation. Right now, the platform is brand new and I’m looking to grow it into a helpful space for honest, early validation. If you’re working on something — or just have an idea in your head — I’d love for you to give it a try: https://www.ideakiln.com Also happy to answer any questions, or chat about where it’s headed. Feedback is always welcome!

Visit product View on Hacker NewsApr 24, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, email · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: personal, month, way · Missing: mobile apps, ios, entrepreneurs

24%24% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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