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Step CI API Testing and Monitoring Tool Now Open Source

Hacker News

Step CI API Testing and Monitoring Tool Now Open Source

Hi, this is Mish and Sebastian. We’re building Step CI, a Continuous Integration platform that lets you test, monitor and secure your APIs. Step CI Command Line Interface and test runner are now open source on GitHub ( https://github.com/stepci/stepci ). Our goal is to build the best tool for managing APIs, so we decided to share our work with the open-source community. We want to make it easy and free to use Step CI, no login required. With Step CI, we offer a simple way to write tests that are programming-language independent and work for different API paradigms (REST, GraphQL, XML). We started working on Step CI after meeting on YC’s Startup School. Mish is responsible for the tech, while Sebastian takes care of business operations. We’d very much appreciate if you could try our product and give us feedback. Thank you to all who have already contibuted! You can also reach out to us via e-mail (hello@stepci.com) or our matrix community ( https://matrix.to/#/#stepci:matrix.org ). Thanks in advance for trying our tool! Mish and Sebastian

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: apis, open · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
72%72% 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: way, para · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface · 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
17%17% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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