I built Reddimon to track link conversions instantly

Hacker News

Hi Hacker News, I’m David, and I built *Reddimon* because I kept wasting time guessing which marketing channels actually bring users. Reddimon lets you create *custom links* for tweets, Reddit posts, or comments, then instantly see which ones bring visitors or signups. You can group links into collections (e.g., “Reddit posts” or “Twitter DMs”) to track performance across campaigns. It’s lightweight, fast, and designed for solo founders or early-stage startups. I’d love to hear: - Does this approach make sense for tracking early growth? - Are there features you’d find essential in this kind of tool? Check it out here: [www.reddimon.com]( https://www.reddimon.com )

Visit product View on Hacker NewsAug 26, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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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.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

20%20% 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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