Keyword monitoring and notification tool for B2B

Hacker News

Octolens is an AI-powered social listening tool, aimed at B2B software companies and solo builders. We search the web and social platforms (e.g., Reddit, Linkedin, X, Hacker News...) for keyword mentions that are important to you and notify you via Slack or Email. We also use AI to score posts based on their relevance to your business. We decided to make our homepage a free version of our tool so people can try it out before buying it if they like it. For example, you can search for: - Customer pain points - Your competitor or your own brand or product - Relevant industry terms That way you can participate in the conversation around your brand or industry. A previous product we worked on had a similar version of this as a feature. Turns out a lot of users just wanted this feature (but better) on its own. So we built Octolens Let us know what you think and if you have any suggestions for more features! :)

Visit product View on Hacker NewsMay 16, 2024

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Actual performance

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

91%91% 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: slack, user, new · Missing: mac, agents, macos

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

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

37%37% 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, way · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%15% 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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