I made AI agent for influencer-led growth

Hacker News

After I tried influencer marketing, I realized the first bottleneck is taking so much time from searching best-fit influencers actually to follow up with them. That is why I leverage llm to summarize personas on social, make it more accurate matching, then use automation to outreach and follow up to send them my requirements, if they like they can pitch me back, so i just need to pick best offers. i also can detect who is influencer in my customer base, so i can send them very custom offers based on their follower size, make it more efficient as flywheel.

Visit product View on Hacker NewsFeb 18, 2025

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Launch Intel predictions

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

74%74% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% 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 · Strong signals: growth · Missing: arr, mrr, revenue

21%21% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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