Paniac AWS Budget Alerts – Attention Devs and DevOps of AWS

Hacker News

Hi HN, I'm excited to introduce Paniac AWS Budget Alerts, a tool designed to make AWS budget alerts effortless and efficient. With this, you can: * Receive Budget Notifications on Telegram in Minutes — No Code Required * Overcome AWS’s 10-Budget Limit with Paniac’s flexible alert system. While AWS limits you to 10, Paniac has no such restriction! * Manage Multiple AWS Accounts under one platform Stay informed and in control of your AWS spending without the hassle. Check it out and let me know what you think!

Visit product View on Hacker NewsAug 24, 2024

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

45%45% predicted probability of success on TrustMRR, 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

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

32%32% 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 · Strong signals: platform, efficient · Missing: plus, intuitive, reviews

23%23% 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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