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Hiring DevOps and SREs – testing troubleshooting live

Hacker News

Hiring DevOps and SREs – testing troubleshooting live

Hi HN We are launching our 3rd startup. Our 1st was a PhD project turned into a startup which simulated public cloud costs before adoption - an example use-case was Disney trying to figure out how much it would cost to render their new movie on AWS and Google, using different configs, RIs vs on-demand, different services. That was PlanForCloud and was acquired by RightScale. Our 2nd startup was even more interesting - we built a public Container-as-a-Service cloud, but in a place where Amazon/Google could not enter - Iran! This grew fast, and was acquired by the Uber of Iran - the world's 5th largest ride hailing app by rides. That was AbarCloud and was acquired by Snapp. If anyone is interested in how you design a public cloud with minimal external dependencies, and continues to function when the underlying internet connections to outside of the country are cut, ask in the questions :) Back to our current idea. When scaling AbarCloud, we needed to hire DevOps & SREs. When hiring developers, to assess their skillsets, we’d use something like HackerRank or Codility. But when hiring DevOps and SREs, where troubleshooting, scaling and failover etc, are important, these assessment tools don’t help. CircuitOps spins up live environments with broken things in it, drops the candidate in, and asks them to troubleshoot, diagnose and fix the issues. All their working is recorded, and (future feature) auto-marked. Try it here: www.CircuitOps.com I’d love your feedback, specifically about: 1 - What is the hardest part of hiring? For anyone who has stopped hiring due to Covid-19, has this impacted DevOps/SRE roles? 2 - What do you think about this style of testing candidates by doing real life scenarios? Have you done a DIY approach to this at all? 3 - If you are a hiring manager, would you use this tool during a live interview, or as a pre-interview filter? How else would you use this tool? Cheers from Scotland, Hassan, Ali & Alistair

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90%90% 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: google, new, using · Missing: mac, agents, macos
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Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
49%49% 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: ios, google · Missing: mobile apps, personal, entrepreneurs
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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