Smarketly–The Marketing CoFounder I Built Because No One Saw My Product

Hacker News

I'm Frederick, a builder from Ghana where our tech ecosystem is still emerging. For years, I created solutions that died in obscurity—not because they weren't good, but because marketing felt like an impossible mountain to climb without resources or expertise. After watching my best ideas disappear into the void, I built what I desperately needed: an AI marketing partner that works like a team member, not just another tool. Smarketly's AI assistant June: Hunts for qualified leads in communities where your users already exist Crafts personalized outreach that doesn't trigger spam filters or cringe reflexes Develops marketing strategies calibrated to your specific growth stage Generates conversion-focused content in your authentic voice I built this because the "build it and they will come" myth kills more promising products than technical challenges ever could—especially for founders outside Silicon Valley networks. This is solving my own deepest pain point as a solo founder. It's helped me reach 3x more potential users in the first month using it than in the previous six months combined. Still rough around the edges, but I'd love feedback from fellow builders who know what it's like to create something great that no one discovers. Try it: smarketly.lema-lema.com

Visit product View on Hacker NewsMay 19, 2025

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Actual performance

3points

2comments

Did not reach leaderboard

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

95%95% 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, using · Missing: mac, agents, macos

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: builder, users · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, 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.

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

21%21% 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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