Measure CO2 emissions of your APIs

Hacker News

Have you ever wondered about the carbon footprint of your API? This week, the 27th climate summit takes place in Egypt. This inspired us to draw attention to the emissions produced by our online behaviour. We introduced some CO2-related features to our API testing tool: - Calculate carbon emissions of an API call - Set up a check to validate that your API endpoint doesn't cross the limit when it comes to emissions - Test all your API endpoints and get an approximate calculation of your API emissions in total We hope that by using these features, you will become more aware of the carbon footprint of your infrastructure and act accordingly. You could for example switch to a sustainable hosting provider. http://github.com/stepci/stepci

Visit product View on Hacker NewsNov 15, 2022

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

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

52%52% 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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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