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TestFast – Simplify Your Startup Validation Process

Hacker News

TestFast – Simplify Your Startup Validation Process

I’m excited to share TestFast with you all. This tool is designed to help startups validate their business ideas, addressing the common issue of lacking product-market fit – a reason 43% of startups fail. With TestFast, you can: - Generate landing pages: Answer a few questions about your idea and get a tailored landing page. - Engage potential customers: Gather feedback through newsletter subscriptions and gauge feedback right away. - Monitor engagement: Track visitors, subscribers, and questionnaire responses to understand your audience’s interest. By providing a way to test and validate your ideas, TestFast helps you make informed decisions and reduce the risk of failure. I would love to hear your thoughts and feedback! Check it out here: www.testfa.st Thanks, Zs

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Actual performance

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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 · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
32%32% 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 · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription, subscribers · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · 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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