I made a pretty cheap marketing breakthrough

Hacker News

After struggling to get my previous products noticed (especially building from Ghana), I created Smarketly to solve my own marketing challenges. What I didn't expect was discovering a surprisingly effective approach while testing it. As a technical founder with limited marketing knowledge, I built June (Smarketly's AI assistant) to help me find communities where potential users hang out and craft targeted outreach. While using my own tool, I discovered something that's working remarkably well: June analyzes successful engagement patterns in niche communities before I post The AI identifies specific language/topics that resonate in each community It then helps me create contributions that match these patterns I follow a 5-7 day "value-first" sequence before mentioning my product Results from my first test with Smarketly itself: 340+ signups from targeted communities in 3 weeks $0 in ad spend (crucial for a bootstrapped founder) 18% conversion to paid tier The key breakthrough wasn't just the AI technology, but the systematic process of analyzing community engagement before contributing. This works particularly well for founders outside established tech networks who can't rely on existing connections. I've built this approach directly into Smarketly now - it identifies relevant communities, analyzes engagement patterns, and helps craft contributions that fit naturally. Would love feedback from other builders who struggle with the marketing side! smarketly.lema-lema.com

Visit product View on Hacker NewsMay 20, 2025

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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

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% 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, existing, ide · Missing: https docs, excited, just released

34%34% 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 · Strong signals: builder, users · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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