Engage with your Twitter followers to build meaningful relationships

Hacker News

I think that most of us spend our time on Twitter usually in rage dumping because we're forced to interact with random people without common interest. I believe if we could interact with people with common interests on Twitter then we can build meaningful relationships with them. Fortunately we do have access to people on Twitter with common interests - Our followers; But it's currently not easy to spot what connects us with our followers as we might not follow back all our followers and even if we do there's no guarantee that we'd see all their tweets or those tweets which matter to us. Hence I've created 'engage followers' and it works by, 1. Monitoring the tweets of our Twitter followers. 2. Classifying their tweets according to our chosen topics of interest. 3. Sending the list of tweets which matches our topics to us via an email digest and/or Auto liking them(So we can check those tweets in the likes section of Twitter). I've seen good results over the past few days since I've launched engage followers, Getting to know about some like minded followers on my personal & product accounts. I hope you'd give it a try and tell me how it can be made more useful.

Visit product View on Hacker NewsJul 6, 2022

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Did not reach leaderboard

Launch Intel predictions

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

77%77% 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: email · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

50%50% 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: personal · Missing: mobile apps, ios, entrepreneurs

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

22%22% 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

19%19% 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.

Incorrect prediction on native model

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