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I'm developing a tiny app that allows users to create AI-agents to filter the web for personalized content. Here’s how it works: users set up profiles describing their interests, like: Profile 1: AI researcher focused on LLMs Profile 2: React developer interested in frontend tech, etc. the agent will match new content to each profile and assigns relevance scores to content based on the profile. Then, it delivers a daily digest of few high-quality, curated links tailored to each user’s interests. so far, I’ve built a feature that generates a structured feed from any webpage. Now, I am writing code for profile setup. . to refine filtering capabilities for each profile for more customized results. does this sound interesting? please check this out and provide your valuable feedback.

Visit product View on Hacker NewsOct 29, 2024

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

92%92% 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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% 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 · Missing: arr, mrr, revenue

18%18% 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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