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Startup Solve – AI toolkit for startup clarity

Hacker News

Startup Solve – AI toolkit for startup clarity

Hey HN, I’m 14 and I’ve been obsessed with startups for a while. I noticed one big problem: most of us (especially first-time founders) waste months building something that nobody wants. So I built Startup Solve — an AI-powered platform that helps you stress-test your startup ideas before you write a single line of code. It includes six tools: ~ AI Co-Founder → brainstorms with you like a smart partner. ~ Startup Oracle → pressure-tests your idea and asks the brutal questions VCs would. ~ Funding Predictor → gives an estimate of whether your idea could raise funding. ~ Idea Incubator → helps refine and niche down broad ideas. ~ Viability Scanner → scores viability, market size, and competition. Growth Engine → generates potential go-to-market strategies. The goal isn’t to replace founders — it’s to save time, sharpen clarity, and help you focus on the right problems instead of chasing noise. I built this without a coding background, learning step by step along the way. It’s still early, but a few people who’ve tried it said it feels like having a “reality-check partner” for their startup. I’d love feedback from HN — both brutally honest criticism and ideas on how to make this more valuable for serious founders. https://startup-solve.lovable.app Thanks for reading.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: lovable, single, coding · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, way, scanner · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
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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