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AWS Savings Delivered Directly to Slack

Hacker News

AWS Savings Delivered Directly to Slack

Hey HN: Kaveh here, founder of www.usage.ai (we help companies cut AWS spend automatically) We've heard from users that engineers don't like having yet-another-dashboard they need to remember to check periodically. This is our attempt at making AWS cost savings even easier: You can instantly approve new cost savings without leaving Slack! In the future, we'd like to develop an even deeper Slack integration so that engineers can interact and ask questions about the cost savings recommendations Usage makes. Have you experienced any issues with your AWS spend? Happy to chat directly: kaveh@usage.ai Blog post: https://blog.usage.ai/usage-launches-slack-support/

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, new · Missing: mac, agents, macos
86%86% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
64%64% 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: users · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
25%25% 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
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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