A tool to track your marketing consistency

Hacker News

Hey HN, I'm a solopreneur and I ship apps like a madman: 7 projects in the last year. I always felt like I was doing marketing, a tweet here, a LinkedIn post there.. but when I looked back, I couldn’t remember what I’d actually done. So I built Marketing Memory for 2 reasons: 1. To make marketing visible and consistent, like tracking commits but for growth 2. To understand which actions actually drive traffic and impact It connects your marketing activity logs with Google Analytics so you can finally see your effort and results in one place. I hope this helps other makers who struggle to stay consistent with marketing. Would love your feedback pls Axel

Visit product View on Hacker NewsOct 21, 2025

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, activity · Missing: mac, agents, macos

89%89% 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

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

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% 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

29%29% 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

23%23% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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