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Validationly – Validate Startup Ideas via Reddit, Twitter and LinkedIn

Hacker News

Validationly – Validate Startup Ideas via Reddit, Twitter and LinkedIn

Hey HN I’ve been building a small tool called *Validationly*. It's designed to help founders and indie hackers validate their startup ideas before writing a single line of code — by checking what the internet is already saying. The concept is simple: You type in your idea (e.g. “AI meeting assistant”) and Validationly pulls related signals from *Reddit*, *Twitter*, and *LinkedIn*, then analyzes the volume and tone to generate a *“validation score.”* You also get a quick snapshot of trending discussions, pain points, and real user language around the idea. What makes it useful: - No more guessing if your idea is “worth building” - Helps avoid the build trap before you spend weeks coding - Bonus: It even drafts a *tweet* and a *Reddit post* based on your idea — ready to share for feedback I built this after running into the same loop: idea → excitement → build → launch → silence. So instead, I wanted a way to quickly test if anyone was already talking about a space — and if there’s real demand. Live here: [ https://validationly.com ]( https://validationly.com ) (no login needed) Ideal for: indie makers, early-stage founders, solo builders Still in early stages, looking for *feedback, brutal honesty, or ideas* on what you'd want next. Thanks for checking it out! – Built by a no-code maker who prefers flow over friction

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: user, single, coding · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder · Missing: plus, platform, intuitive
30%30% 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
24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
14%14% predicted probability of success on TrustMRR, 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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