Validationly Update – Recommended Tools via Partnerships for Founders

Hacker News

Hi HN, A month ago I shared Validationly: a tool that helps founders validate startup ideas using signals from Twitter, Reddit, and LinkedIn. Today I’m launching a big update: - *Recommended Tools via Partnerships:* SaaS founders can now add their products as recommended tools inside Validationly. - *Goal:* when someone tests a startup idea, they can also discover relevant tools that might help them build faster. - *Focus:* it’s meant as founder-to-founder recommendations, not ads. I’d love feedback from the community: - Is this valuable for founders? - Which categories of tools would be most useful? - How can we keep this system healthy and not spammy? Thanks!

Visit product View on Hacker NewsAug 31, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

73%73% 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: using · Missing: mac, agents, macos

71%71% 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: lua, ide, io · Missing: https docs, excited, just released

36%36% 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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

22%22% 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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