Website monitoring/tracking with multi LLM agents and email alerts

Hacker News

We have created a website tracker called Thieves. Thieves is a personal AI agent that periodically checks websites for your set conditions in natural human language. Once your condition is met you receive an email. Websites can be checked every 1 hour or every 24 hours. You can invite anyone else to receive your email alerts as well. Examples -"Notify me when the price of this product drops below $50." -"Email me if a new blog post or news article is published with the keyword 'AI'." -"Let me know when this restaurant's online menu adds a 'vegan' section." -“Notify me if a new version number is posted on this specific page for a cybersecurity standard” Please let us know what you think and how to improve! What would you use it for? Which areas should we improve. You can try it free here https://negativestarinnovators.com/pricing/pricing.html

Visit product View on Hacker NewsFeb 26, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

73%73% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, 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

44%44% 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

42%42% 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

40%40% 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

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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