LivingInbox – Stay updated on prospects with unmatched recommendations

Hacker News

Hey Folks! LivingInbox enables sales-related teams to view and engage with only the relevant messages from prospects across channels. It surfaces content that is semantically relevant (not just keywords) to your free-form alerts, while taking into consideration your product information and lead list. Reply to posts across channels from LivingInbox with AI-assisted suggestions. Always be on top of your messages, whether it is a noisy slack channel where your prospects post messages, that one LinkedIn post by a lead among a sea of posts, or an email from an existing lead. Here’s the gist of how it works: 1.Add your communities like Slack, Linkedin, and tools like Email, SMS, etc. 2.Enter your product info, lead list, criteria like "mentioned CRM", "blog on outbound", etc. 3.Get alerts, view relevant messages, and get AI assistance to reply from LivingInbox. Sign up for early access and get discounted pricing. We look forward to hearing from y'all :)

Visit product View on Hacker NewsMar 26, 2024

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, inbox, email · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% 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

33%33% 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 · Missing: plus, platform, intuitive

26%26% 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

16%16% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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