I made a tool to find purchase-ready leads from LinkedIn, X, and Reddit

Hacker News

Hey everyone, I’ve just rebuilt my SaaS, Replyhub[1], a tool to find high-potential leads from Reddit, X, and LinkedIn. The tool scans relevant posts related to your business, analyzes them, and identifies potential leads. It then alerts you so you can either reach out to them or engage with the posts directly. Here’s the backstory: When I first launched Replyhub three months ago, it was designed to monitor relevant discussions on Reddit and automatically reply to them. The goal was to streamline engagement and surface potential leads. However, several issues quickly arose. The AI-generated auto-replies started disrupting discussions, making them less natural and less helpful. Additionally, some users exploited the tool by creating new Reddit accounts to spam discussions with automatic replies. To address these problems and maintain community standards, Reddit began shadow-banning these new accounts. Faced with these challenges, I decided to rethink the approach. I removed the auto-reply feature entirely, shifting to a model where users can manually engage with discussions. This adjustment fosters more meaningful and respectful interactions, allowing users to contribute value to conversations and introduce their services only when it’s truly relevant. In the latest update, I’ve expanded Replyhub to include X/Twitter and LinkedIn. It now monitors real-time discussions across all three platforms, helping you find potential leads who are ready to purchase a solution to their problem. Any feedback or advice is welcome, and I look forward to hearing your thoughts. [1] https://replyhub.co

Visit product View on Hacker NewsSep 12, 2024

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

92%92% 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: model, user, new · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, 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

47%47% 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: platform, users · Missing: plus, intuitive, reviews

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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