An AI marketing tool for non-marketing people

Hacker News

Marketing is one of the hardest parts to figure out for small businesses and solopreneurs. So we built Markebility, a tool designed to make marketing easier by using AI to walk users through the full marketing journey. The tool helps generate a simple strategy, weekly content plan (focused on social media for now), and generates posts for those channels. We’re finalizing integrations with the social platforms now. Once that’s done, users will be able to schedule and publish directly from the platform. The goal is to also pull in analytics from those posts so we can improve the strategy over time and recommend better-performing content and channels. Right now, the strategy, content planner and content generator are live and free to try. We still have a ways to go, but this is the most complete version we've shipped since early MVPs so we're looking for feedback. Particularly on the onboarding flow, clarity of value, or anything that feels off or missing. If you have any ideas on what would make this tool even better, we'd love to hear it. Thanks for checking it out!

Visit product View on Hacker NewsMay 20, 2025

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2points

Did not reach leaderboard

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

82%82% 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

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal

58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

Acquire.comPre-revenue stage for this audience · 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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